Facilitating revenue generation from wholesale electricity markets using an engineering-based model

ABSTRACT

The disclosure facilitates generation of energy-related revenue for an energy customer of an electricity supplier. The disclosure herein can be implemented to generate suggested operating schedules for energy assets that include a controllable energy asset, using an objective function. The objective function is determined based on a dynamic simulation model of the energy profile of the energy assets. The dynamic simulation model is adaptive to physical changes in the energy assets based at least in part on a physical model of the thermodynamic property of the at least one energy asset and at least in part on data representative of an operation characteristic of the controllable energy asset, a thermodynamic property of the energy assets, and/or a projected environmental condition. Energy-related revenue available to the energy customer is based at least in part on a wholesale electricity market or on a regulation market.

BACKGROUND

In various regions across the United States, “regional transmissionoperators” (RTOs) or “independent system operators” (ISOs) generally areresponsible for obtaining electricity from electricity generators (e.g.,operators of coal-fired plants, gas plants, nuclear plants,hydroelectric plants, renewable resources, etc.), and then transmittingthe electricity provided by generators over particular geographicregions (e.g., New England, the greater New York area, the mid-Atlanticstates) via an electricity transmission infrastructure (also commonlyreferred to as the electricity “grid”). RTOs generally are responsiblefor regional planning of grid expansion and/or ordering deployment ofnew electricity transmission infrastructure by transmission owners.

The Federal Energy Regulation Commission (FERC) presently requires that,in addition to generally managing the operation of the electricity gridin a given geographic area, RTOs/ISOs need to manage the price ofelectricity generated and consumed on the grid via “wholesaleelectricity markets.” To this end, RTOs/ISOs establish pricing auctionsto provide and support wholesale electricity markets. These pricingauctions, in addition to setting wholesale prices as a function of time,also foster sufficient electricity production for the grid at variouslocations to ensure that the grid is capable of delivering adequateelectricity to respective locations of demand for electricity on thegrid. Thus, some of the key objectives of the RTOs/ISOs in overseeingwholesale electricity markets include providing for efficient, economicand reliable operation of the grid.

In general, a given RTO/ISO supports a wholesale electricity market byallowing competing electricity generators to offer their electricityproduction output to the RTO/ISO. Retail electricity suppliers, alsocommonly referred to as “utilities,” in turn supply electricity toend-users/consumers, or “energy customers” of the retail electricitysuppliers, and are billed by the RTO/ISO for their purchases. Withrespect to the wholesale electricity market, the retail electricitysuppliers make bids for the electricity production output offered by theelectricity generators that, once accepted, establish market prices. Theretail electricity suppliers in turn typically re-price the electricitythey purchase from electricity generators on the wholesale market tosell to their retail electricity customers.

One significant issue facing RTOs/ISOs relates to various limitationsthat exist in connection with the grid that may impede a sufficient flowof electricity on the grid under certain circumstances. In particular,there may be time-dependent and/or geographically-dependent limitationson the grid's ability to support transmission of electricity, based onone or more of: 1) an available overall supply of electricity fromelectricity generators; 2) overall demand from retail electricitysuppliers; 3) general conditions on the grid itself (e.g., aging,failing or dated equipment); and 4) “location-specific” or “congestion”issues, e.g., respective geographic locations on the grid of electricitygenerators, electricity consumers, particular demand conditions, and/orparticular grid-related conditions that in some manner impede thetransmission of available electricity to one or more portions of thegrid). In some circumstances, a grid limitation may be caused by aparticular branch of the grid reaching a thermal limit, or a failure ofa generator or transformer on a branch of the grid; these limitationsgenerally are referred to as “security constraints” (i.e., particulargrid infrastructure cannot be overloaded without jeopardizing the grid).As such, the electricity grid is sometimes referred to as a “securityconstrained system.”

In view of the foregoing, RTOs/ISOs may employ a process known as“security constrained economic dispatch” for establishing wholesaleelectricity prices on a wholesale electricity market. Pursuant to thisprocess, an RTO/ISO managing a particular geographic region of anelectricity grid determines particular locations on the grid, or“nodes,” at which there is a possibility for security constraints tolimit electricity transmission. Wholesale electricity prices as afunction of time are then established independently for each node (i.e.,on a geographically-dependent, or “locational” basis) by accepting bidsfrom energy generators in sequence from the lowest priced offer to thehighest priced offer, up to an amount of electricity needed to satisfyelectricity demand conditions (e.g., bids from retail electricitysuppliers) at the node, so as to develop a supply and demand equilibriumprice. In this manner, the wholesale electricity price at a particularnode reflects the highest-priced accepted generation offer needed toprovide an adequate amount of electricity to that node, taking intoconsideration various security constraints that may be present at thenode. This location-based approach to wholesale electricity prices,which takes into consideration security constraints on the grid,commonly is referred to as “locational marginal pricing,” and thewholesale electricity price at a given node is commonly referred to aLocational Marginal Price (LMP). Thus, the wholesale electricity pricegenerally varies at different locations on the grid, based at least inpart on security constraints.

While electricity generators and retail electricity suppliers make up asignificant constituency of the participants in wholesale electricitymarkets, applicable market rules in some wholesale electricity marketsalso permit electricity consumers/end-users (e.g., energy customers ofretail electricity suppliers) and others to participate in wholesaleelectricity markets so as to earn energy-related revenue and offsettheir energy-related expenditures. In particular, market rules nowpermit energy users (or their market representatives) to make offers tocurtail or otherwise alter their electricity use, or to sellself-generated or stored electricity, to the wholesale market. If suchan offer by an energy customer to provide an “electricity-relatedproduct or service” is accepted on the applicable wholesale market, thecustomer endeavors to appropriately control its various energy assets soas to make available to the grid the offered product/service, in returnfor payment pursuant to the terms of the offer. The concept of an energycustomer providing an electricity-related product or service (e.g.,electricity use curtailment) on a wholesale electricity market inexchange for payment to the energy customer by the RTO/ISO, commonly isreferred to as “demand response” (DR).

Some of the currently more active wholesale electricity sub-markets inwhich energy customers of retail service providers may readilyparticipate include the “energy markets” (e.g., “day-ahead” energymarket, “real-time dispatched” energy market). While various pricingmodels exist for participation in these markets and other economicdemand response wholesale electricity markets (as well as variouspenalty models for customer non-performance pursuant to an offer toreduce/curtail energy use), often any revenue generated by the energycustomer from participation in these markets is based on the locationalmarginal price (LMP). The LMP may be calculated periodically atspecified nodes (e.g., every 5 minutes, every half-hour, every hour)depending on the particular market in which the energy customer isparticipating. More generally, revenue generation relating toparticipation in an economic demand response wholesale electricitymarket is based on a prevailing “wholesale electricity price” for theparticular market in question, which in turn generally is based on theLMP (calculated at various intervals), as discussed above.

To determine revenue earned by participating energy customers in aparticular economic demand response wholesale electricity market such asan “energy market,” the amount of electricity use reduction by theparticipating customer typically has to be measured; subsequently, thismeasured amount of electricity use reduction typically is multiplied bya price relating to the prevailing wholesale electricity price for themarket in question (e.g., LMP). Electricity use reduction by the energycustomer conventionally is measured against a reference electricityusage commonly referred to as a “customer baseline” (CBL). The CBL isintended to represent what the participating energy customer'selectricity use normally would have been, over a particular time periodand typical (“business-as-usual” or BAU) operating conditions for thecustomer's energy assets, absent the customer's voluntary electricityuse reduction based on the incentive provided by the economic demandresponse wholesale electricity market.

Conventionally, a customer baseline (CBL) electricity use profile for anenergy customer is derived by an RTO/ISO from an historical sample ofactual electricity use by the customer over a particular time period andBAU operating conditions. In some cases, the particular time period forwhich an historical sample of the customer's actual electricity use isselected as a CBL may be based, at least in part, on similar conditionsprevailing at the customer's site at the time of the historical samplingand participation in the economic demand response program (e.g., similarweather conditions, similar seasons/time of year, similar occupancyconditions at the customer's site, etc.). In other instances, the timeperiod for selecting an historical sample of actual electricity usage asa CBL is based on relatively recent actual electricity use by the energycustomer just prior to the customer's participation in the economicdemand response program. For example, the ISO PJM Interconnectcalculates a market-participating customer's CBL for a given weekday as“the average of the highest four out of the five most recent highestload (electricity use) weekdays in the 45 calendar day period precedingthe relevant load reduction event.” In sum, revenue generation from theeconomic demand response wholesale electricity “energy markets”conventionally is based on an historical actual electricity usage of aparticipating customer, which historical actual electricity usage servesas a customer baseline (CBL) against which electricity use reduction ismeasured for purposes of paying the energy customer for the usereduction.

SUMMARY

The Inventors have recognized and appreciated that new opportunities forparticipation in wholesale electricity markets by electricityconsumers/end-users (e.g., energy customers of retail electricitysuppliers) have created a need for energy management tools to facilitateenergy-related revenue generation from such markets. In view of theforegoing, various embodiments are directed generally to methods,apparatus and systems for determining operating schedules for energyassets so as to facilitate revenue generation from wholesale electricitymarkets. These energy assets include energy storage assets, energyconsuming assets and energy generating assets. In different examplesherein, an energy asset can include an energy storage asset, an energyconsuming asset, and/or an energy generating asset.

Wholesale electricity markets in which the energy customer mayparticipate to earn energy-related revenue, and to which the variousmethods, apparatus and systems according to the concepts disclosedherein may apply, include various economic demand response wholesaleelectricity markets, examples of which include, but are not limited to,a “real-time energy market,” a “day-ahead energy market,” a “day-aheadscheduling reserve market,” a “synchronized reserve” market, a“regulation” market, a “capacity” market, and an “emissions” market. Thevarious methods, apparatus and systems according to the conceptsdisclosed herein may also apply to facilitate the energy customerparticipating in a market based on a voltage/VAR ancillary service toearn energy-related revenue. In some examples, the methods, apparatusand systems described herein may be implemented in whole or in part by acurtailment service provider (CSP) or other entity acting as a “broker”between energy customers and an RTO/ISO to facilitate participation invarious demand response programs supported by wholesale electricitymarkets.

Suggested Operating Schedules for Energy Assets

In example implementations discussed in greater detail below, themethods, apparatus and systems described herein determine a suggestedoperating schedule for one or more energy assets (includingenergy-consuming assets for which energy usage may be curtailed), over agiven time period T, that are operated by an energy customer of a retailelectricity supplier. The energy assets operated by the energy customermay include electricity-consuming assets as well aselectricity-generating assets (e.g., fossil-fuel-based generators,renewable energy sources) and/or electricity storage assets (e.g.,batteries). The time period T over which a suggested operating schedulefor the energy asset(s) may be determined according to the inventiveconcepts disclosed herein may be a portion of an hour, an hour, a periodof multiple hours, a day, or a period of multiple days, for example(which in some instances may be based, at least in part, on time-varyingwholesale electricity prices on a particular wholesale electricitymarket from which revenue may be generated). Similarly, the suggestedoperating schedule(s) for the energy assets(s) may be determined basedat least in part on wholesale prices of various wholesale electricity“products” offered on the wholesale electricity markets in which theenergy customer may participate (e.g., based on a geographic region inwhich the energy customer is located) to earn energy-related revenue.

In one example implementation, as discussed in greater detail below, thesuggested operating schedule for one or more energy assets is determinedvia a mathematical optimization process that reduces a netenergy-related cost to the energy customer over the time period T byincreasing projected energy-related revenue from one or more wholesaleelectricity markets in which the energy customer may participate.

Energy Asset Modeling

To facilitate the mathematical optimization process for generating asuggested operating schedule for one or more energy assets, amathematical model representing the customer's energy asset(s) isformulated and employed in the mathematical optimization process. Theenergy asset model is specified by one or more mathematical functionsfor calculating an energy profile (i.e., electricity use and/orelectricity generation as a function of time over the time period T) forthe asset(s), based on a proposed operating schedule for the asset(s)applied as an input to the model. In one example of an engineering-basedmodel, the mathematical function(s) defining the asset model at least inpart represent physical attributes of the energy asset(s) themselvesthat relate to electricity use and/or electricity generation. Dependingon the energy asset(s) operated by the energy customer, a given modelmay represent a single energy asset or an aggregation of multiple energyassets operated by the customer.

Also, depending on the type of energy asset(s) being modeled, the assetmodel may be formulated to accept additional inputs to facilitatecalculation of an energy profile based on a proposed operating schedule.Herein, in various examples, energy storage assets, energy consumingassets and/or energy generating assets are being modeled. For example,in the case of controllable energy assets, including energy consumingassets such as building assets including heating, ventilation and airconditioning (HVAC) systems for temperature control in one or morebuildings, and/or other assets for which thermodynamic considerationsare relevant (including weather- or temperature-dependent energygenerating assets including photovoltaic cells and wind turbines), themathematical model for the asset(s) may be configured to consider as aninput to the model actual or forecast ambient environmental conditions(e.g., temperature, humidity, ambient light/cloud cover, etc.) as afunction of time, as well as other variables that may impactthermodynamics or the energy profile in general (e.g., buildingoccupancy, a presence of equipment such as computers and otherinstrumentation that may affect heating or cooling in an environment,etc.).

Customer Baseline (CBL) Energy Profiles for Business-As-Usual (BAU)Operating Schedules

In some examples, the mathematical model for the energy asset(s) firstis used to generate a simulated (or “predictive”) customer baseline(CBL) energy profile corresponding to a typical operating schedule (alsoreferred to herein as a “business-as-usual” (BAU) operating schedule, or“BAU conditions”). In particular, an energy customer's BAU operatingschedule for its energy asset(s) is applied to the mathematical model,which in turn provides as an output a simulated CBL energy profilerepresenting a typical electricity consumption or generation as afunction of time, over a given time period T, for the modeled energyasset(s). In one aspect, the energy customer's BAU operating schedulerepresents the customer's typical behavior with respect to operating itsenergy asset(s), absent any incentive to reduce energy costs and/or earnenergy-related revenue from the wholesale electricity market.

As discussed in greater detail below, a simulated and predictive CBLenergy profile based on a mathematical model according to the conceptsdisclosed herein provides a significant improvement over conventionalapproaches to determine a frame of reference for typical energy profilesof energy customers (absent an incentive to generate revenue viawholesale electricity markets); as noted above, conventional approachesare limited to considering only historical actual energy useinformation. In particular, it is recognized and appreciated herein thatconventional backward-looking assessment of CBL is not necessarilyrepresentative of what an energy customer's electricity usage actuallywould have been on a given day for which economic demand responserevenue is being calculated—at best, such backward-looking historicalactual-use-based assessments of CBL provide inconclusive estimates.

Additionally, it has been observed empirically that an historicalactual-use CBL provides incentives for some energy customers toartificially inflate energy usage (i.e., by not operating energy assetspursuant to “business-as-usual” or BAU conditions, but insteadpurposefully adopting higher-consumption operating conditions) prior toa period in which the customer anticipates participation in economicdemand response wholesale electricity markets; an artificially higherhistoric actual-use-based CBL, against which energy use reduction willbe measured, provides a potentially higher economic demand responserevenue. In this manner, the general goal of economic demand responseprograms to incentivize reduced electricity usage is undermined (by anartificially-increased electricity usage to establish a higher CBL).

Furthermore, the Inventors have recognized and appreciated that anhistorical actual-use-based CBL provides a long-term disincentive toparticipate in economic demand response wholesale electricity markets.In particular, as a given energy customer participates in economicdemand response wholesale electricity markets over time, their averageactual electricity use from retail suppliers is expected to decrease. Ifrevenue from such markets continues to be calculated with reference toan historical actual-use-based CBL, the potential for economic demandresponse revenue will decrease over time, as an economic settlementapproach based on historical actual-use CBL eventually will begin totreat incentivized electricity use reduction as “business-as-usual”operating conditions for the energy customer. This type of treatmentarguably will ultimately discourage participation in wholesaleelectricity markets. At very least, continued reliance on historicalactual-use-based CBL likely will compel an extension of a “look-back”period serving as a basis for determining CBL for energy customers whoactively participate in economic demand response wholesale electricitymarkets for significant periods of time. As longer look-back periods areadopted, the accuracy and relevance of historic actual-use-based CBLsfrom more distant time periods arguably will significantly decrease.

Accordingly, for at least the foregoing reasons, a simulated andpredictive CBL energy profile, based on a mathematical model of anenergy customer's energy asset(s) according to the concepts disclosedherein (rather than an historical actual-use-based CBL as conventionallyemployed), provides a significant improvement for more accuratelydetermining revenue earned from economic demand response wholesaleelectricity markets. In some examples, the mathematical model for theenergy asset(s) may not be predicated on any significantly historicalactual electricity use information for the energy asset(s), and insteadmay be based in part on physical attributes of the energy asset(s)themselves that relate to electricity use and/or electricity generation(such as for an engineering-based building asset model), as noted above.In this manner, simulated and predictive CBL energy profiles based onsuch mathematical models are not substantively influenced bysignificantly historical actual electricity use information.

An engineering-based energy asset model according to a principle hereinmay adapt itself to the current conditions of an energy asset. That is,the computation of the CBL calculations may reflect temporary changes orpermanent changes in the physical characteristics of an energy asset(such as but not limited to building insulation). Non-limiting examplesof such changes include but are not limited to changes in the buildingmaterials or building construction, changes in the types of energyassets present in the system, the types of temperature control systemspresent in the system. The historical actual-use-based CBL may capturepermanent changes in the building asset as well

In other examples, the mathematical model for energy asset(s) may bepredicated on some degree of essentially real-time or near real-timefeedback (e.g., from one or more control systems actually controllingthe modeled energy asset(s)), which feedback may represent actualelectricity use. This feedback may be used, according to some examplesof the methods, apparatus and systems disclosed herein, to refine someaspects of the mathematical model; however, even when real-time or nearreal-time feedback representing actual electricity use is employed, insome examples the mathematical model is may be based on physicalattributes of the energy asset(s) themselves relating to electricity useand/or electricity generation (such as for an engineering-based energyasset model described herein).

Objective Cost Functions

In some examples, the mathematical model for the energy asset(s) isemployed to determine a suggested operating schedule over a given timeperiod T for the energy asset(s) (different than the BAU operatingschedule) based on a mathematical optimization of an “objective costfunction” representing the net energy-related cost to the energycustomer for operating the asset(s). In example implementations, theobjective cost function incorporates the mathematical model for theenergy asset(s) and specifies energy-related revenues from one or morewholesale energy markets (e.g., based on forecasted wholesale energyprices over the time period T for the one or more wholesale markets ofinterest), from which possible revenue may be available to the energycustomer. In some examples, the energy-related revenues specified in theobjective cost function may take into consideration a simulated customerbaseline (CBL) energy profile (discussed above) as a basis fordetermining such revenue.

The objective cost function employed in the mathematical optimization todetermine a suggested operating schedule for the energy asset(s) alsomay specify energy-related costs which are offset by the energy-relatedrevenues. In particular, in some examples, the energy-related costsincluded in the objective cost function may include “actual”energy-related costs. Non-limiting examples of the “actual”energy-related costs include retail electricity costs, wholesaleelectricity costs representing revenue earned by the energy customer,fuel costs to run one or more electricity generation assets, operationand/or maintenance costs that may be associated with electricitygeneration and/or energy storage assets, lifetime and/or replacementcosts for electricity generation and/or energy storage assets,emissions-related costs, tariffs that can be leveled in the industry,demand charges that can be leveled at times of peak energy usage, etc.The energy-related costs included in the objective cost functionadditionally or alternatively may include “indirect” energy-relatedcosts, such as convenience/comfort costs associated with the energycustomer's adoption of a suggested operating schedule different than theBAU operating schedule (the convenience/comfort cost represents an“indirect” cost associated with a change in the customer's behavior withrespect to operating its asset(s), based on the incentive of possibleenergy-related revenue from the wholesale electricity markets). Theenergy-related costs may also include a reliability cost (such as basedon any voltage/VAR control activity in a microgrid application) and/oran emissions cost based on an emissions market.

Optimization of Objective Cost Function For Generating Energy AssetOperating Schedules

In one example, the objective cost function (which incorporates themathematical model of the energy asset(s)) may be provided to anoptimizer (a particularly-programmed processor, also referred to as a“solver”) that implements a mathematical optimization process todetermine a suggested operating schedule for the energy asset(s) over agiven time period T. In one conceptual illustration of the mathematicaloptimization process, some number N of candidate operating schedules aresuccessively applied to the mathematical model to generate simulatedenergy profiles corresponding to the candidate operating schedules. Anet energy-related cost represented by the objective cost function iscalculated for each simulated energy profile, and the candidateoperating schedule that minimizes the objective cost function (i.e.,minimizes the net energy-related cost) is selected as the suggestedoperating schedule. In some implementations, the amount of revenueavailable from the relevant wholesale electricity markets over the giventime period T is a significant factor dictating the candidate operatingschedule that is provided as an output of the optimizer. Theenergy-related costs may also include a reliability cost (such as basedon any voltage/VAR control activity in a microgrid application) and/oran emissions cost based on an emissions market.

Adopting Operating Schedules, Market Bids and Settlement

The suggested operating schedule in turn may be transmitted to theenergy customer (e.g., to an energy management system of the energycustomer, including a building management system), and the customer maychoose to adopt or not adopt the suggested operating schedule toactually operate its energy asset(s) over the particular time period Tfor which the optimization is performed. In some implementations, agiven operating schedule is transmitted to the energy customer in theform of one or more bias signals representing a change in an operatingset point of one or more assets, as a function of time over the timeperiod T, from the typical or “business-as-usual” (BAU) operating setpoint for the asset(s). In some examples, the energy customer makes achoice to adopt a given suggested operating schedule in tandem withmaking an offer (a “bid”) to provide one or more wholesale electricitymarket products to the appropriate market pursuant to the adoptedoperating schedule.

If the energy customer adopts the suggested operating schedule toactually operate its energy asset(s) so as to provide a particularwholesale electricity market product pursuant to an accepted bid (e.g.,reduce its energy consumption), various information ultimately isobtained from the energy customer to facilitate a “settlement” processpursuant to which the customer is paid by the wholesale market operator(i.e., the RTO/ISO overseeing the wholesale electricity market(s) inwhich the customer is participating). For example, in one examplerelating to energy markets (wherein the “product” is energy usecurtailment), the energy customer's “metered load” (i.e., actual energyuse during the time period T in which the suggested operating scheduleis adopted) is measured, and compared to a simulated CBL based on themathematical model for the customer's energy asset(s). The energycustomer may then be paid for its economic demand response electricityuse reduction based on a difference between the simulated CBL and theactual metered load, multiplied by the actual wholesale energy priceduring the time period T for the market in question (e.g., LMP).

Apparatus, methods and computer-readable media are described fordetermining a suggested operating schedule for at least one energy assetoperated by an energy customer. In an example, the apparatus includes atleast one communication interface, at least one memory to storeprocessing unit-executable instructions and an objective function forthe at least one energy asset, and at least one processor unitcommunicatively coupled to the at least one memory. The at least oneenergy asset includes at least one controllable energy asset. Theobjective function facilitates a determination of the suggestedoperating schedule for the at least one energy asset based at least inpart on a physical model of the thermodynamic property of the at leastone energy asset and at least in part on data representative of modelparameters. The model parameters are: (a) an operation characteristic ofthe at least one controllable energy asset, and (b) a projectedenvironmental condition during time period T. Upon execution of theprocessing unit-executable instructions, the at least one processingunit: (A) prior to time period T, determines the suggested operatingschedule based on an optimization of the objective function over timeperiod T, and (B) controls the at least one communication interface totransmit to the energy customer the suggested operating scheduledetermined in (A), and/or controls the at least one memory so as tostore the determined suggested operating schedule. The objectivefunction is determined based on a dynamic simulation model of the energyprofile of the at least one energy asset, a customer baseline (CBL)energy profile for the at least one energy asset, and a forecastwholesale electricity price associated with a wholesale electricitymarket and/or a regulation price associated with a regulation market,over time period T. The dynamic simulation model is adaptive to physicalchanges in the at least one energy asset based at least in part on thephysical model of the thermodynamic property of the at least one energyasset, and the dynamic simulation model is trained using the data. Theoperation of the at least one energy asset according to the suggestedoperating schedule, over a time period T, facilitates generation ofenergy-related revenue based at least in part on the wholesaleelectricity market.

In an example, the CBL energy profile is computed based on applying thedynamic simulation model to the data representative of the operationcharacteristic of the at least one controllable energy asset, thethermodynamic property of the building asset, and an environmentalcondition, all during a time period TA prior to time period T.

In an example, the data representative of the projected environmentalcondition is data representative of at least one of an ambienttemperature of the environment in which the building asset is located; ahumidity of the environment in which the building asset is located; anamount of solar irradiance of the environment in which the buildingasset is located, an amount of cloud cover of the environment in whichthe building asset is located, an outside air temperature, an outsideair humidity, an outside air enthalpy, an outside air wet bulbtemperature, a dewpoint temperature, and a heat index.

In an example, the at least one energy asset comprises at least onebuilding asset. In an example implementation, the physical model of thethermodynamic property of the at least one building asset is determinedbased on at least in part on state-space model computations of the atleast one building asset, and on data representative of at least one ofan occupancy schedule of the building asset, a relative humidity of thebuilding asset, a temperature of the building asset, and a lightinglevel of the building asset.

In an example, the physical model of the thermodynamic property of theat least one energy asset models a zone temperature, and the zonetemperature is computed based on at least one heat-balance state-spacemodel of the at least one energy asset.

In an example, the at least one processing unit determines the suggestedoperating schedule for the at least one energy asset as at least onebias signal, as an interruptible load function, or as at least one usemodulation signal. In this example, the at least one processing unit candetermine the suggested operating schedule for the at least one energyasset in (A) as at least one bias signal, and controls the at least onecommunication interface in (B) to transmit to the energy customer the atleast one bias signal at different times during time period T. In thisexample, the at least one processing unit controls the at least onecommunication interface to transmit to the energy customer the suggestedoperating schedule as at least one use modulation signal, and theoperation of the at least one energy asset according to the at least oneuse modulation signal causes a modulation with time of the load use ofthe controllable energy asset.

In an example implementation, the dynamic simulation model of the energyprofile of the at least one energy asset can be a semi-linear regressionover at least one of the model parameters. In this example, the dynamicsimulation model of the energy profile of the at least one energy assetcan be a semi-linear regression over at least one of a zone temperatureof the at least one energy asset, a load schedule of the at least oneenergy asset, the projected environmental condition, and a controlsetpoint of the at least one controllable energy asset. The zonetemperature of the at least one energy asset can be a semi-linearregression over at least one of the projected environmental condition,the load schedule of the at least one energy asset, and the controlsetpoint of the at least one controllable energy asset.

In an example implementation, the dynamic simulation model is trainedusing the data representative of the operation characteristic of the atleast one controllable energy asset and a thermodynamic property of theat least one energy asset, during operation of the at least one energyasset at under similar environmental conditions to the projectedenvironmental condition during time period T.

In an example implementation, the dynamic simulation model of the energyprofile of the at least one energy asset can include an environmentalcondition-independent load model and an environmentalcondition-dependent load model.

In an example implementation, the at least one processing unit canfurther: (C) determine an updated operating schedule for the at leastone energy asset, during time period T, based on updated parameters ofthe suggested operating schedule, and (D) controls the at least onecommunication interface to transmit to the energy customer the updatedoperating schedule for at least one energy asset determined in D),and/or controls the at least one memory so as to store the determinedupdated operating schedule. The updated parameters are determined usinga feedback mechanism. The feedback mechanism comprises comparing apredicted value of at least one of the model parameters, computed usingthe suggested operating schedule, to an actual value measured, duringtime period T, of the respective at least one of the model parameters.

In an example implementation, the updated parameters of the suggestedoperating schedule are computed based on Kalman filtering.

In an example implementation, the at least one processing unitdetermines the updated operating schedule for the at least one energyasset as at least one bias signal, as an interruptible load function, oras at least one use modulation signal.

Apparatus, methods and computer-readable media are described formodeling of a building asset. The building asset models generated usingthe apparatus, methods and computer-readable media described hereinfacilitate revenue generation from wholesale electricity markets. Thewholesale electricity market may be an energy market, a regulationmarket, and/or a spinning reserve market. The revenue generation fromthe wholesale electricity markets is based on the output of the buildingasset model. In an example, the apparatus includes at least onecommunication interface, at least one memory to storeprocessor-executable instructions and a mathematical model, and at leastone processing unit. The mathematical model is used to generate thebuilding asset model according to a method described herein, based atleast in part on at least one operation characteristic of the buildingasset. The at least one processing unit is communicatively coupled tothe at least one communication interface and the at least one memory.Upon execution of the processor-executable instructions, the at leastone processing unit determines the building asset model using themathematical model. Input to the mathematical model includes at leastone operation characteristic of the building asset. Input to themathematical model may include the time period being modeled, and/orweather-related parameters, including outside air temperature (OAT),outside air humidity (OAH), solar irradiance (Q_(irradiance)), and/orcloud cover. Upon execution of the processor-executable instructions,the at least one processing unit also controls the at least onecommunication interface to transmit to the energy customer thedetermined building asset model, and/or output generated based on thebuilding asset model. In an example, the output of the building assetmodel include values for building zone air temperature and/or aset-point (SPT) for one or more building components. In a non-limitingexample, the building zone air temperature is a return-air-temperature(RAT). In an example, the output of the mathematical model include otherparameters that are determined based on the output of the building assetmodel such as but not limited to including the building zone airtemperature (including a RAT), and/or the SPT. Output of themathematical model can include a projected value of energy usage.

In another example, a building asset model described herein can be anengineering-based model. An example engineering-based model describedherein can be a two-node model that provides improvement inthermodynamic and HVAC predictions with automated parameter search andvalidation. An engineering-based model described herein may provide animprovement on a physical model, including a HVAC system model (such asa chiller model, AHU model, ice-storage model, etc.). An exampleengineering-based model described herein is applicable to a well-sensedbuildings (for which there is data on its operation characteristics,including the operation characteristics of one or more of itscomponents).

Apparatus, methods and computer-readable media are also described forgenerating a building asset model to determine operation parameters andoperating schedules for at least one building asset. In an example, theapparatus includes at least one communication interface, at least onememory to store processor-executable instructions and a mathematicalmodel, and at least one processing unit. The mathematical model is usedto generate the building asset model according to a method describedherein, based at least in part on at least one operation characteristicof the building asset. The at least one processing unit iscommunicatively coupled to the at least one communication interface andthe at least one memory. Upon execution of the processor-executableinstructions, the at least one processing unit determines the buildingasset model using the mathematical model. Input to the mathematicalmodel includes at least one operation characteristic of the buildingasset.

In one aspect, apparatus, methods and computer-readable media aredescribed for generating a day-ahead building asset model to determineoperation parameters and/or operating schedules for at least onebuilding asset. The apparatus includes at least one communicationinterface, at least one memory to store processor-executableinstructions and a day-ahead building asset model, and at least oneprocessing unit. The day-ahead building asset model is used to generateoperation parameters and operating schedules for at least one buildingasset according to a method described herein, based at least in part onat least one operation characteristic of the building asset. The atleast one processing unit is communicatively coupled to the at least onecommunication interface and the at least one memory. Upon execution ofthe processor-executable instructions, the at least one processing unitdetermines the operation parameters and/or operating schedules for atleast one building asset using the day-ahead building asset model. Inputto the day-ahead building asset model includes at least one operationcharacteristic of the building asset. For example, input to theday-ahead building asset model may include the time period beingmodeled, and/or weather-related parameters, including outside airtemperature (OAT), outside air humidity (OAH), solar irradiance(Q_(irradiance)), and/or cloud cover. Upon execution of theprocessor-executable instructions, the at least one processing unit alsocontrols the at least one communication interface to transmit to theenergy customer the determined operation parameters and/or operatingschedules. In an example, the operation parameters and/or operatingschedules include values for building zone air temperature (including areturn-air-temperature (RAT)) and/or a set-point (SPT) for one or morebuilding components. Output of the day-ahead building asset model can beused to determine a projected value of energy usage. The output of theday-ahead building asset model can be used to project a day-aheadexpected performance of at least one component and/or system of thebuilding asset when they are later operated.

In one aspect, apparatus, methods and computer-readable media aredescribed for generating a real-time building asset model to determineoperation parameters and/or operating schedules for at least onebuilding asset. The apparatus includes at least one communicationinterface, at least one memory to store processor-executableinstructions and a real-time building asset model, and at least oneprocessing unit. The real-time building asset model is used to generateoperation parameters and operating schedules for at least one buildingasset according to a method described herein, based at least in part onat least one operation characteristic of the building asset. The atleast one processing unit is communicatively coupled to the at least onecommunication interface and the at least one memory. Upon execution ofthe processor-executable instructions, the at least one processing unitdetermines the operation parameters and/or operating schedules for atleast one building asset using the real-time building asset model. Inputto the real-time building asset model includes a measured value of atleast one operation characteristic of the building asset duringreal-time operation. For example, input to the real-time building assetmodel may include weather-related parameters, including outside airtemperature (OAT), outside air humidity (OAH), solar irradiance(Q_(irradiance)), and/or cloud cover. Upon execution of theprocessor-executable instructions, the at least one processing unit alsocontrols the at least one communication interface to transmit to theenergy customer the determined operation parameters and/or operatingschedules. In an example, the operation parameters and/or operatingschedules include values for building zone air temperature (including areturn-air-temperature (RAT)) and/or a set-point (SPT) for one or morebuilding components. Output of the real-time building asset model canused to determine a projected value of energy usage. The output of thereal-time building asset model can be used to adjust or modify one ormore operating schedules of the building asset when it is beingoperated. In an example, the output of the real-time building assetmodel can be used to adjust or modify one or more operating schedules ofthe building asset when it is being operated according to an output of aday-ahead building asset model.

In one aspect, apparatus, methods and computer-readable media aredescribed for generating a day-after building asset model to determineactual operation parameters of at least one building asset. Theapparatus includes at least one communication interface, at least onememory to store processor-executable instructions and a day-afterbuilding asset model, and at least one processing unit. The day-afterbuilding asset model is used to generate operation parameters for atleast one building asset according to a method described herein, basedat least in part on at least one operation characteristic of thebuilding asset. The at least one processing unit is communicativelycoupled to the at least one communication interface and the at least onememory. Upon execution of the processor-executable instructions, the atleast one processing unit determines the operation parameters for atleast one building asset using the day-after building asset model. Inputto the day-after building asset model includes a measured value of atleast one operation characteristic of the building asset during a periodof operation of at least one component and/or system of the buildingasset. For example, input to the day-after building asset model mayinclude the time period during which the building asset was operated,and/or weather-related parameters, including outside air temperature(OAT), outside air humidity (OAH), solar irradiance (Q_(irradiance)),and/or cloud cover. Upon execution of the processor-executableinstructions, the at least one processing unit also controls the atleast one communication interface to transmit to the energy customer thedetermined actual operation parameters. In an example, the actualoperation parameters include values for actual load and/or actual energyusage of the building asset. In an example, the day-after building assetmodel can be used to determine actual operation parameters of at leastone building asset that was operated according to operating schedulesdetermined using an output of a day-ahead building asset model and/or anoutput of a real-time building asset model. The operation parametersdetermined using the day-after building asset model can be used forsettlement purposes, e.g., to compute an amount of energy-relatedrevenue due to the energy customer. Output of the day-after buildingasset model can used to determine a projected value of energy usage.

Apparatus, methods and computer-readable media are described forapplying a different building asset models at different stages duringoperation of at least one component and/or system of the building asset.In different examples, the building asset model is a day-ahead buildingasset model, a real-time building asset model, or a day-after buildingasset model. The apparatus, methods and computer-readable media can beused to generate the day-ahead building asset model, and/or to apply theday-ahead building asset model to input parameters characteristic of thebuilding asset to project a day-ahead expected performance of at leastone component and/or system of the building asset prior to operation. Inan example, the output of the day-ahead building asset model is used incomputation of an objective cost function. In an example, the output ofthe day-ahead building asset model is used in computation of anenergy-related revenue. The apparatus, methods and computer-readablemedia can be used to generate the real-time building asset model, and/orto apply the real-time building asset model to model a real-timeperformance of at least one component and/or system of the buildingasset during the time period that they are operated. The apparatus,methods and computer-readable media can be used to generate theday-after building asset model, and/or to apply the day-after buildingasset model to model an actual performance of at least one componentand/or system of a building asset after they have been operated. In anexample, the output of the day-after building asset model is used incomputation of an objective cost function. In an example, the output ofthe day-after building asset model is used in computation of anenergy-related revenue.

Apparatus, methods and computer-readable media are also described forapplying a building asset model, including a day-ahead building assetmodel, a real-time building asset model, or a day-after building assetmodel, to determine operation parameters and, where pertinent, operatingschedules, for controllers of one or more components of the buildingasset.

Apparatus, methods and computer-readable media are also described forgenerating a seasonal building asset model. For example, the buildingasset model developed to model the building asset for a summer seasoncan differ from the building asset model developed to model the buildingasset for a fall, winter, or spring season, based on the differingenvironmental and weather conditions and responses thereto. In anexample, a seasonal building asset model is generated by varyingtemperature-related terms. The seasonal building asset model can bedeveloped as a day-ahead building asset model, a real-time buildingasset model, or a day-after building asset model, to determine operationparameters and operating schedules for the building asset or for one ormore components of the building asset. In another example, the seasonalbuilding asset model can be developed as a day-ahead building assetmodel, a real-time building asset model, or a day-after building assetmodel, to determine operation parameters and, where pertinent, operatingschedules, for the controllers of one or more components of the buildingasset.

Apparatus, methods and computer-readable media described herein can beused for determining an operating schedule of at least one controller ofat least one energy asset so as to generate energy-related revenue, overa time period T, associated with operation of the at least one energyasset according to the operating schedule. The energy-related revenueavailable to the energy customer over the time period T is based atleast in part on at least one wholesale electricity market. Theapparatus includes at least one communication interface, at least onememory to store processor-executable instructions and a mathematicalmodel for the at least one energy asset, and at least one processingunit. The mathematical model facilitates a determination of theoperating schedule for the controllers based at least in part on anoperation characteristic of the at least one energy asset and forecastwholesale electricity prices associated with the at least one wholesaleelectricity market. The at least one processing unit is configured todetermine the operating schedule for the at least one controller of theat least one energy asset using the mathematical model by minimizing anet energy-related cost over the time period T. The net-energy relatedcost is based at least in part on at least one energy supply cost and atleast one demand response revenue. The operating schedule specifies,during a time interval within the time period T, conditions for use ofthe at least one energy asset in respective ones of the at least oneenergy market. The at least one processing unit is also configured tocontrol the at least one communication interface to transmit to theenergy customer the operating schedule for the at least one controllerof the at least one energy asset, control the at least one memory so asto store the determined operating schedule for the at least onecontroller, and/or control the at least one communication interface totransmit to the at least one controller of at least one energy asset theoperating schedule. The energy asset can include at least one of anenergy storage asset, an energy generating asset, an energy consumingasset, or any combination thereof. The at least one wholesaleelectricity market can be at least one of an energy market, a regulationmarket, a spinning reserve market, or any combination thereof. Theforecast wholesale electricity prices associated with the energy marketcan be a wholesale price. The forecast wholesale electricity pricesassociated with the regulation market can be a regulation price. Theforecast wholesale electricity prices associated with the spinningreserve market can be a spinning reserve market price.

At least one computer-readable storage medium is provided herein. The atleast one computer-readable storage medium is encoded with instructionsthat, when executed using at least one processor unit described herein,performs a method according to a principle described herein. Forexample, the method can be for modeling a building asset and/or forapplying a building asset model to operation characteristics of abuilding asset to generate an operating schedule for the building assetor for a controller of at least component of the building asset.

The following patent applications are hereby incorporated herein byreference in their entirety:

-   U.S. Provisional Application No. 61/477,067, filed on Apr. 19, 2011;    -   U.S. Provisional Application No. 61/552,982, filed on Oct. 28,        2011;    -   U.S. Non-provisional application Ser. No. 12/850,918, filed on        Aug. 5, 2010; and    -   U.S. Provisional Application No. 61/279,589, filed on Oct. 23,        2009.

The entire disclosure of these applications is incorporated herein byreference in its entirety, including drawings,

It should be appreciated that all combinations of the foregoing conceptsand additional concepts discussed in greater detail below (provided suchconcepts are not mutually inconsistent) are contemplated as being partof the inventive subject matter disclosed herein. In particular, allcombinations of claimed subject matter appearing at the end of thisdisclosure are contemplated as being part of the inventive subjectmatter disclosed herein. It should also be appreciated that terminologyexplicitly employed herein that also may appear in any disclosureincorporated by reference should be accorded a meaning most consistentwith the particular concepts disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The skilled artisan will understand that the drawings primarily are forillustrative purposes and are not intended to limit the scope of theinventive subject matter described herein. The drawings are notnecessarily to scale; in some instances, various aspects of theinventive subject matter disclosed herein may be shown exaggerated orenlarged in the drawings to facilitate an understanding of differentfeatures. In the drawings, like reference characters generally refer tolike features (e.g., functionally similar and/or structurally similarelements).

FIG. 1 shows an example system that includes an energy asset, accordingto a principle described herein.

FIG. 2 shows an example system that includes an energy asset and acontroller, according to a principle described herein.

FIG. 3 shows an example apparatus according to a principle describedherein.

FIG. 4 illustrates an example block diagram representing an asset modelaccording to a principle described herein.

FIG. 5 illustrates an example block diagram representing another assetmodel according to a principle described herein.

FIG. 6 illustrates an example block diagram representing another assetmodel according to a principle described herein.

FIG. 7 illustrates an example block diagram representing another assetmodel according to a principle described herein.

FIG. 8 illustrates an example block diagram representing another assetmodel according to a principle described herein.

FIG. 9 summarizes a non-limiting example of a day-aheadengineering-based model, a real-time engineering-based model, and aday-after engineering-based model, according to a principle describedherein;

FIG. 10 shows an example of an effective element representation of abuilding asset according to a principle described herein.

FIG. 11A-11E show the example nodes of the effective elementrepresentation of FIG. 10, according to a principle described herein.

FIG. 12A-12B show example matrices for solving for the coefficients ofeffective element representations of a building asset according to aprinciple described herein.

FIG. 13 shows flowcharts of example procedures for generating andtraining dynamic simulation load models and dynamic simulationtemperature models according to a principle described herein.

FIG. 14 shows an example Kalman Filter process according to a principledescribed herein.

FIG. 15 shows an example day-after engineering-based model based on aneural network model according to a principle described herein.

FIG. 16 shows an example day-after engineering-based model based on aneural network model implemented in a parallel architecture and aseries-parallel architecture according to a principle described herein.

FIG. 17 is an example plot showing the relationship between differentvalues of the comfort index (CI), the outside air humidity and thehumidity according to a principle described herein.

FIG. 18 shows an example functional block diagram of a PID controlleraccording to a principle described herein.

FIG. 19 shows an example functional block diagram of a configuration ofa system and apparatus for implementing an example CBL computationaccording to a principle described herein.

FIG. 20 shows an example functional block diagram of a configuration ofa system and apparatus for implementing an example day-aheadengineering-based model according to a principle described herein.

FIG. 21 shows an example functional block diagram of a configuration ofa system and apparatus for implementing an example real-timeengineering-based model according to a principle described herein.

FIG. 22 shows an example functional block diagram of a configuration ofa system and apparatus for implementing an example day-afterengineering-based model according to a principle described herein.

FIG. 23 illustrates a block diagram of an example energy managementenvironment that includes an energy management system to facilitategeneration of revenue from wholesale electricity markets, according to aprinciple described herein;

FIG. 24 illustrates a block diagram showing additional details of theexample energy management system of FIG. 23, according to a principledescribed herein;

FIG. 25 illustrates a block diagram of an example schedule buildermodule of the example energy management system of FIG. 24, according toa principle described herein;

FIG. 26 shows an example of an implementation based on an examplesuggested operating schedule, according to a principle described herein.

FIG. 27 shows an example energy storage asset optimization according toa principle described herein.

DETAILED DESCRIPTION

Following below are more detailed descriptions of various conceptsrelated to, and embodiments of, inventive methods, apparatus, andsystems for determining a suggested operating schedule for energy assetsto facilitate revenue generation from wholesale electricity markets. Thesystems, methods and apparatus described herein can be applied to abuilding asset. The output of the different models also can be used fordetermining a suggested operating schedules for the building asset. Itshould be appreciated that various concepts introduced above anddiscussed in greater detail below may be implemented in any of numerousways, as the disclosed concepts are not limited to any particular mannerof implementation. Examples of specific implementations and applicationsare provided primarily for illustrative purposes.

As used herein, the term “includes” means includes but not limited to,the term “including” means including but not limited to. The term “basedon” means based at least in part on.

As used herein, the term “in communication with” includes directcommunication between elements as well as indirect communication betweenthe elements, such as by means of at least one intermediate component.As used herein, the term “in electrical communication with” includesdirect electrical communication between elements as well as indirectelectrical communication between the elements, such as by means of atleast one intermediate component.

The methods, apparatus, and systems disclosed herein provide resourcesby which a certain environment that includes one or more energy assetsis modeled, energy use and/or generation profiles of the assets may besimulated based on the model(s), and operating schedules for the energyasset(s) may be suggested, based on such simulations, to facilitaterevenue generation from one or more wholesale electricity markets forenergy customers overseeing the environment (e.g., operating the energyasset(s) within the environment). Environments including one or more ofa wide variety of energy assets are contemplated, examples of whichinclude, but are not limited to, a building or group of buildings thatinclude one or more energy-consuming and/or energy-generating assets(e.g., heaters, HVAC systems, chillers/ice makers, fossil-fuel-basedand/or renewable electricity generators, energy storage devices), aswell as other environments in which one or more building may not beinvolved (but which may nonetheless include one or more energy assets).

In some examples, optimization software constituting an “optimizer”module (also referred to as a “solver” or simply “optimizer”) is used toperform an optimization process to determine suggested operatingschedules for one or more energy assets that minimize net energy-relatedcosts for the energy customer. In one aspect, the optimization processis based at least in part on balancing respective energy-related costsand energy-related revenues in connection with the energy customer'soperating environment and assets therein, in consideration of wholesaleelectricity prices as a function of time. In other aspects, an“objective cost function” that represents the net energy-related cost asa function of an operating schedule for the energy asset(s) and amathematical model for the energy asset(s) may include multipleconstituent components, examples of which include, but are not limitedto:

-   -   1. a convenience or comfort cost associated with deviation from        the CBL or business-as-usual (BAU) operating schedule,    -   2. a cost associated with electric power production by the        customer's energy generating assets (if any),    -   3. a cost associated with electric power supply from a retail        electricity supplier, and    -   4. a cost associated with economic demand response (DR) revenue        from one or more wholesale electricity markets.

Accordingly, salient aspects of example methods, apparatus and systemsaccording the principles described herein are to provide energy assetmanagement capabilities for reducing retail electricity costs byoptimizing electricity usage, generation, and storage, while at the sametime providing significant revenue opportunities in markets, includingwholesale electricity markets, in regulation markets, in synchronizedreserve markets and/or in emissions markets.

In an example, an optimization module of an example energy managementsystem according to the principles described herein can be used toperform an optimization process. In a non-limiting example, theoptimization process can be performed as a staged process. In a stage ofthe optimization process, a computation can be performed to calculatehow a system including at least one energy asset responds on a normalday using the default settings of the energy assets and with no outsideintervention. The result can be used to determine a business-as-usual(BAU) schedule. A customer baseline (CBL) schedule can be derived basedon applying a mathematical model to the BAU schedule. Alternatively, apredetermined CBL or BAU schedule may be introduced into theoptimization process rather than calculated. Additionally, theoptimization may be used to determine the financial impact with respectto the CBL schedule or the BAU schedule. In another stage of theoptimization process, an optimization can be run with certain operatingchanges that may be entered by a system operator of the energymanagement system. This stage may also factor in energy prices and/oremissions trading prices. Again, the optimization module may be used todetermine the financial impact of the optimized operating schedule,preferably showing the financial benefit thereof as compared with a BAUschedule.

The apparatus and methods described herein are applicable to a systemthat includes an energy asset 1. The energy asset 1 can be a buildingasset. In the non-limiting example of FIG. 1, the energy asset 1 is incommunication with a power grid 2 (as depicted in the example of FIG.1). As depicted in the non-limiting example of FIG. 1, the energy asset1 may be located behind a power meter 3. For example, the energy asset 1may be one or more facilities of the energy consumer.

The apparatus and methods described herein are also applicable to asystem that includes an energy asset 1 and a controller 4 incommunication with at least one component of the energy asset 1, wherethe energy asset 1 and the controller 4 are in communication with apower grid 2 (as depicted in the example of FIG. 2). The controller 4facilitates operating the one or more components of the energy asset 1according to an operating schedule and/or operation parameters generatedusing a building asset model described herein. As depicted in thenon-limiting example of FIG. 2, the controller 4 and the energy asset 1may be located behind a power meter 5. For example, the energy asset 1may be one or more facilities of the energy consumer and the controller4 may be located at any of the one or more facilities.

In any of the apparatus, methods, and/or computer readable mediadescribed herein, the building asset model can be an engineering-basedmodel. For example, any of the day-ahead building asset models, thereal-time building asset models, or day-after building asset modelsdescribed herein can be an engineering-based model. In an example, acombination of various types of engineering-based models may be applieda given building asset as a day-ahead building asset model, a real-timebuilding asset model, or a day-after building asset model. Variousexamples and aspects of engineering-based building asset models that areapplicable to any of the apparatus, methods, and/or computer readablemedia are described herein.

The output of the building asset models described herein may be used tooperate an energy asset in a way that facilitates generation ofenergy-related revenue for an energy customer of an electricitysupplier. The energy customer may commit an amount of energy or energycurtailment from the at least one energy asset to an energy market. Inan example, the electricity supplier may be a retail electricitysupplier that supplies the electricity to the energy customer at aretail price. In another example, the electricity supplier may supplythe electricity to the energy customer at a contracted for or negotiatedprice. In various examples herein, the energy customer may allow anamount of capacity of the energy asset to be committed to the energymarket.

In a non-limiting example, an apparatus or a method described herein canbe used to generate an operating schedule for a controller thatcommunicates with the energy asset and/or at least one component of theenergy asset. The controller is capable of exercising an amount ofcontrol over the energy asset and/or at least one component of theenergy asset. Operation of the controller, and hence the energy assetand/or at least one component of the energy asset, according to theoperating schedule generated by a principle herein over the specifiedtime period may generate for the energy customer an amount ofenergy-related revenue based at least in part on a wholesale electricitymarket.

A non-limiting example of the apparatus 10 according to the principlesdescribed herein is illustrated in FIG. 3. The apparatus 10 includes atleast one communication interface 11, at least one memory 12, and atleast one processing unit 13. The at least one processing unit 13 iscommunicatively coupled to the at least one communication interface 11and the at least one memory 12.

The at least one memory 12 is configured to store processor-executableinstructions 14 and a mathematical model 15. In a non-limiting example,the mathematical model 15 can be used to generate the building assetmodel according to a method described herein, based at least in part onat least one operation characteristic of the building asset. In anothernon-limiting example, the mathematical model can be used to determine anoperating schedule for a controller of one or more components of abuilding asset based on data 16 associated with parameters, includingbut not limited to, an operation characteristic of the energy asset anda forecast wholesale electricity price associated with the wholesaleelectricity market.

In any of the examples according to the principles described herein, themathematical model 15 can include the dynamic simulation model.

In an example, the wholesale electricity price for a market differsaccording to the time period in the day, e.g., during on-peak timesversus off-peak times. In an example, the forecast wholesale electricityprices used in the computation of the day-ahead building asset model maybe the same as the wholesale electricity prices used for the real-timemodel and for the day-after model. In another example, the forecastwholesale electricity prices used in the computation of the day-aheadbuilding asset model may differ from the wholesale electricity pricesused for the real-time model, which in turn may differ from thewholesale electricity prices used for the day-after model. In anexample, the wholesale electricity price is computed as a budget oractual market prices on the day that the systems of the building assetis run, as compared to a forecasted wholesale electricity price for theday-ahead model. In an example, the wholesale electricity prices,including the forecast wholesale electricity prices, may be set by amarket operator (such as but not limited to a RTO), or other third partyprovider.

In a non-limiting example, the at least one processing unit 13 executesthe processor-executable instructions 14 stored in the memory 12 atleast to determine the building asset model using the mathematical model15. The at least one processing unit 13 also executesprocessor-executable instructions 14 to control the communicationinterface 11 to transmit to the energy customer 17 the building assetmodel that has been determined and/or controls the memory 12 to storethe determined building asset model.

In an example, the at least one processing unit 13 executes theprocessor-executable instructions 14 stored in the memory 12 at least todetermine the building asset model using the mathematical model 15 as anengineering-based model. An example engineering-based model describedherein can be a single-node or a two-node model that providesimprovement in thermodynamic and HVAC predictions with automatedparameter search and validation. An engineering-based model describedherein may provide an improvement on a physical model, including a HVACsystem model (such as a chiller model, AHU model, ice-storage model,etc.). In an example, the engineering-based model includes one or moreequations and/or parameters that are based on physical components and/orsystems in the buildings. For example, an engineering-based model can bedeveloped to take into account the thermodynamics of some components(e.g., using a thermodynamic equation for a HVAC), the heat conductionor other energy exchange of the building asset based on a temperaturedifference between the inside and outside of the building (modeled as acircuit element equation). An example engineering-based model describedherein is applicable to a sensed buildings (for which there is data onits operation characteristics, including the operation characteristicsof one or more of its components). In another example, anengineering-based model described herein is applicable to a sensedbuildings for which some amount of input data for an operationcharacteristic of one or more components may be inferred (i.e.,estimated or projected).

Upon execution of the processor-executable instructions, the at leastone processing unit also controls the at least one communicationinterface to transmit to the energy customer the determined buildingasset model, and/or output generated based on the building asset model.In an example, the output of the building asset model includes valuesfor building zone air temperature (including a return-air-temperature(RAT)) and/or a set-point (SPT) for one or more building components. Inan example, the output of the mathematical model include otherparameters that are determined based on the output of the building assetmodel such as but not limited to the building zone air temperature(including a RAT) and/or the SPT. Output of the mathematical model caninclude a projected value of energy usage.

In a non-limiting example, the at least one processing unit 13 executesthe processor-executable instructions 14 stored in the memory 12 atleast to generate a building asset model to determine operationparameters and/or operating schedules for at least one building asset.The mathematical model 15 is used to generate the building asset modelaccording to a method described herein, based at least in part on atleast one operation characteristic of the building asset. Upon executionof the processor-executable instructions 14, the at least one processingunit 13 determines the building asset model using the mathematical model15. Input to the mathematical model 15 includes at least one operationcharacteristic of the building asset.

In a non-limiting example, other possible input to the model includeinformation about weather condition, solar radiance, degree of cloudcover, outside air temperature, or outside air humidity.

The at least one operation characteristic of the building asset in anyof the building asset models described herein can be at least one of azone temperature, a prior load, a HVAC schedule, a control setpoint, ora HVAC temperature.

The determined operation parameters and/or operating schedules for theat least one building asset can be at least one of a building zone airtemperature (including a return air temperature (RAT)), a setpoint(SPT), a zone temperature, a load, or an HVAC schedule. In any of theexamples described herein, the RAT can be used as an indicator of thezone temperature (T_(z)).

In one aspect, the at least one processing unit 13 executes theprocessor-executable instructions 14 stored in the memory 12 at least togenerate a day-ahead building asset model to determine operationparameters and/or operating schedules for at least one building asset.The day-ahead building asset model is used to generate operationparameters and operating schedules for at least one building assetaccording to a method described herein, based at least in part on atleast one operation characteristic of the building asset. Output of theday-ahead building asset model can used to determine a projected valueof energy usage. The output of the day-ahead building asset model can beused to project a day-ahead expected performance of at least onecomponent and/or system of the building asset when they are lateroperated.

In one aspect, the at least one processing unit 13 executes theprocessor-executable instructions 14 stored in the memory 12 at least togenerate a real-time building asset model to determine operationparameters and/or operating schedules for at least one building asset.The real-time building asset model is used to generate operationparameters and operating schedules for at least one building assetaccording to a method described herein, based at least in part on atleast one operation characteristic of the building asset. Output of thereal-time building asset model can used to determine a projected valueof energy usage. The output of the real-time building asset model can beused to adjust or modify one or more operating schedules of the buildingasset when it is being operated. In an example, the output of thereal-time building asset model can be used to adjust or modify one ormore operating schedules of the building asset when it is being operatedaccording to an output of a day-ahead building asset model.

In one example, the at least one processing unit 13 executes theprocessor-executable instructions 14 stored in the memory 12 at least togenerate a day-after building asset model to determine actual operationparameters of at least one building asset. The day-after building assetmodel is used to compute actual operation parameters for at least onebuilding asset according to a method described herein, based at least inpart on at least one operation characteristic of the building asset. Inan example, the actual operation parameters include values for actualload and/or actual energy usage of the building asset. In an example,the day-after building asset model can be used to determine actualoperation parameters of at least one component or system of a buildingasset that was operated according to operating schedules determinedusing an output of a day-ahead building asset model and/or an output ofa real-time building asset model. Output of the day-after building assetmodel can used to determine a projected value of energy usage. Theoperation parameters determined using the day-after building asset modelcan be used for settlement purposes, e.g., to compute an amount ofenergy-related revenue due to the energy customer. For example, theday-after building asset model can be used to model actual readings ofcomponents or systems of a building asset when, e.g., the HVAC is run,the zone temperature (including the RAT) is measured, electricity used,and so can be used to model the actual energy usage of a building asset.

In one example, the at least one processing unit 13 executes theprocessor-executable instructions 14 stored in the memory 12 at least toapply different building asset models at different stages duringoperation of at least one component and/or system of the building asset.In different examples, the building asset model is a day-ahead buildingasset model, a real-time building asset model, or a day-after buildingasset model. The at least one processing unit 13 may execute theprocessor-executable instructions 14 stored in the memory 12 at least togenerate the day-ahead building asset model, and/or to apply theday-ahead building asset model to input parameters characteristic of thebuilding asset to project a day-ahead expected performance of at leastone component and/or system of the building asset and/or operatingparameters of the building asset prior to operation of at least onecomponent and/or system of the building asset. In an example, the atleast one processing unit 13 may execute the processor-executableinstructions 14 stored in the memory 12 at least to compute an objectivecost function based on the output of the day-ahead building asset model.In an example, the at least one processing unit 13 may execute theprocessor-executable instructions 14 stored in the memory 12 at least tocompute an energy-related revenue based on the output of the day-aheadbuilding asset model for settlement purposes. The at least oneprocessing unit 13 may execute the processor-executable instructions 14stored in the memory 12 at least to generate the real-time buildingasset model, and/or to apply the real-time building asset model to modela real-time performance of at least one component and/or system of thebuilding asset during the time period that the building is operated (andpossibly modify the operating schedule based on application of Kalmanfiltering to update the model). The at least one processing unit 13 mayexecute the processor-executable instructions 14 stored in the memory 12at least to generate the day-after building asset model, and/or to applythe day-after building asset model to determine an actual performance ofat least one component and/or system of a building asset after they havebeen operated. In an example, the at least one processing unit 13 mayexecute the processor-executable instructions 14 stored in the memory 12at least to compute an objective cost function based on the output ofthe day-after building asset model. In an example, the at least oneprocessing unit 13 may execute the processor-executable instructions 14stored in the memory 12 at least to compute an energy-related revenuebased on the output of the day-after building asset model for settlementpurposes.

In a non-limiting example, the day-ahead building asset model, thereal-time building asset model and the day-after building asset modelare applied in time-order sequence to a single building asset orcollection of building assets.

In another non-limiting example, the day-ahead building asset model, thereal-time building asset model and the day-after building asset modelare applied to a single building asset or collection of building assetsin the same given day. For example, on a given day the at least oneprocessing unit 13 may apply to the building asset (or collection ofbuilding assets):

(i) a day-after building asset model to determine the operating schedulefor the following day, to project a day-ahead expected performance of atleast one component and/or system of the building asset and/or operatingparameters of the building asset;(ii) a real-time building asset model possibly to modify the operatingschedule based on application of Kalman filtering to update the model asthe given day progresses; and(iii) a day-after building asset model to determine the actualperformance of at least one component and/or system of a building assetbased on its operation the previous day, e.g., with the computation ofenergy-related revenue, for settlement purposes.

In an example, the at least one processing unit 13 executes theprocessor-executable instructions 14 stored in the memory 12 at least toapply a building asset model, including a day-ahead building assetmodel, a real-time building asset model, or a day-after building assetmodel, to determine operation parameters and, where pertinent, operatingschedules, for controllers of one or more components of the buildingasset.

In a non-limiting example, the at least one processing unit 13 executesthe processor-executable instructions 14 stored in the memory 12 atleast to determine the operating schedule for the controller of theenergy asset using the mathematical model 15. The at least oneprocessing unit 13 also executes processor-executable instructions 14 tocontrol the communication interface 11 to transmit to the energycustomer 17 the operating schedule that has been determined for thecontroller and/or controls the memory 12 to store the determinedoperating schedule for the controller. In a non-limiting example, theprocessing unit 13 may execute processor-executable instructions 14 tocontrol the communication interface 11 to transmit to the operatingschedule directly to the controller.

In a non-limiting example, the at least one processing unit 13 executesthe processor-executable instructions 14 stored in the memory 12 atleast to determine an operating schedule of at least one controller ofat least one energy asset so as to generate energy-related revenue, overa time period T, associated with operation of the at least one energyasset according to the operating schedule. The energy-related revenueavailable to the energy customer over the time period T is based atleast in part on at least one wholesale electricity market. Themathematical model 15 facilitates a determination of the operatingschedule for the controllers based at least in part on an operationcharacteristic of the at least one energy asset and forecast wholesaleelectricity prices associated with the at least one wholesaleelectricity market. The at least one processing unit 13 is configured todetermine the operating schedule for the at least one controller of theat least one energy asset using the mathematical model 15 by minimizinga net energy-related cost over the time period T. The net-energy relatedcost is based at least in part on at least one energy supply cost and atleast one demand response revenue. The operating schedule specifies,during a time interval within the time period T, conditions for use ofthe at least one energy asset in respective ones of the at least oneenergy market. The at least one processing unit 13 is also configured tocontrol the at least one communication interface 11 to transmit to theenergy customer 17 the operating schedule for the at least onecontroller of the at least one energy asset, control the at least onememory so as to store the determined operating schedule for the at leastone controller, and/or control the at least one communication interfaceto transmit to the at least one controller of at least one energy assetthe operating schedule. The energy asset can include at least one of anenergy storage asset, an energy generating asset, an energy consumingasset, or any combination thereof. The at least one wholesaleelectricity market can be at least one of an energy market, a regulationmarket, a spinning reserve market, or any combination thereof. Theforecast wholesale electricity prices associated with the energy marketcan be a wholesale price. The forecast wholesale electricity pricesassociated with the regulation market can be a regulation price. Theforecast wholesale electricity prices associated with the spinningreserve market can be a spinning reserve market price.

The operation characteristic of the energy storage asset may be itsstate of charge, charge rate, the degree of non-linearity of the chargerate, discharge rate, degree of non-linearity of the discharge rate,round trip efficiency, and degree of life reduction. In an example wherethe operation characteristic of the energy storage asset is its chargerate and/or discharge rate, the operating schedule for the controllermay include suggested different time intervals for charging the energystorage asset or discharging the energy storage asset during the timeperiod T that the system is in operation.

The optimized operating schedule according to the systems, apparatus andmethods described herein is based on an optimization of an objectivecost function over an entire time period T. The optimized scheduleresults from the maximization of the economic benefit, given constraintson the controls or on the states, or more generally from theminimization or maximization of an objective function. As a non-limitingexample, the suggested operating schedule may calls for acounter-intuitive action, such as sending a signal to a controller toincrease a load, e.g., to charge an energy storage asset (such as abattery) or increase the load of another type of controllable energyasset (such as a HVAC), even during a time interval when the forecastprices are expected to be higher. The optimization according to theprinciples described herein evaluate the various actions and responsesof the energy assets over the entire time period Tin order to optimizethe objective cost function, e.g., to maximize the economic benefit,given constraints on the controls or on the states. Accordingly,implementation of a suggested operating schedule generated according tothe system, method or apparatus described herein can result in actionsthat differ from more heuristic approaches.

In an example, the operation characteristic of an energy asset may beits load use schedule. For example, the operation characteristic of theenergy asset can be its energy consumption profile as a function oftime. The energy consumption characteristics of a controllable energyasset may be modified by changing parameters of operation of the system.A non-limiting example of an operation characteristic for a controllableenergy asset is its set point. The set point may be a controllable setpoint, e.g., it may be controllable as a function of time ortemperature. For example, where the controllable energy asset is abuilding with a variable internal temperature controlled by a heating,ventilation and air conditioning (HVAC) system, the operationcharacteristic may be a temperature set point for the HVAC system. Inanother example, the operation characteristic may be a setting of thecomponent. For example, where the component is a HVAC or a chiller, theoperation characteristic may be a cooling rate/speed of the HVAC or achill rate of the chiller, respectively.

As described herein, in an example, an amount of energy of the energystorage asset may be generated and supplied to the power line at adischarge rate to generate energy-related revenue for the energycustomer in an energy market. The energy-related revenue can depend on aforecast wholesale electricity price associated with the wholesaleelectricity market, and may be determined based on computation of anet-energy related cost. The net energy related cost may be computedbased on the supply costs for supplying electricity to the customer anda demand response revenue. An apparatus and method herein can beimplemented to generate an operating schedule for the controller of theenergy storage asset that provides recommendations for the timing ofcharging and discharging of the energy storage asset.

In an example, the processing unit 13 can be configured to determine theoperating schedule for the controller of the at least one energy assetusing the mathematical model 15 by minimizing a net energy-related costover a relevant time period (T). The net energy-related cost can beassociated with electricity generation by the energy storage asset,electricity consumption by the energy storage asset, and electricityconsumption by the energy consuming asset. Here, the energy-relatedrevenue available to the energy customer may be computed based at leastin part on the minimized net energy-related cost.

The net energy-related cost may be specified as a difference between theelectricity supply cost and the economic demand response revenue overthe pertinent time period.

In an example, the processing unit 13 can be configured to determine theoperating schedule for the controller using the mathematical model and arepresentative customer baseline (CBL) energy profile for the energyconsuming asset over the time period (T). As used herein, the term“representative customer baseline energy profile” or “representative CBLenergy profile” encompasses representations of the energy customer'senergy usage in the absence of change of behavior according to theprinciples described herein. As non-limiting examples, the“representative customer baseline energy profile” or “representative CBLenergy profile” includes an estimation based on the energy customer'sbusiness-as-usual (BAU) operations, including any form of averaged orweighted measure based on measures of historical BAU operations. Herein,the representative CBL energy profile represents a typical operation ofthe at least one energy consuming asset by the energy customer. Forexample, where the energy consuming asset is a fixed-load asset, therepresentative CBL may be determined as the energy consumption profilefor the energy consuming asset.

Where the operating schedule for the controller is generated based onusing the mathematical model and a representative customer baseline(CBL) energy profile, the economic demand response revenue may becomputed based on the forecast wholesale electricity price, theelectricity generation by the energy storage asset, the electricityconsumption by the energy storage asset, and the representative CBLenergy profile for the energy consuming asset. The values of economicdemand response revenue computed according to a principle herein may beused for settlement purposes. For example, the values of economic demandresponse revenue computed according to a principle herein may beprovided to an energy supplier for settlement purposes.

In an example herein, a portion of the energy of the energy storageasset may be committed to the regulation market. That is, the wholesaleelectricity market for the energy customer would include an energymarket and a regulation market. In an example where the forecastwholesale electricity price is for the energy market, the operatingschedule for the controller may specify optimal time intervals for useof the energy storage asset in the regulation market. As a non-limitingexample, the suggested operating schedule may calls for acounter-intuitive action, such as committing a portion of the energy ofthe energy storage asset to the regulation market even during a timeinterval when the forecast wholesale electricity price are expected tobe higher than the potential revenue from the regulation market duringthat time. The optimization according to the principles described hereinevaluate the various actions and responses of the energy assets over theentire time period Tin order to optimize the objective cost function,e.g., to maximize the economic benefit, given constraints on thecontrols or on the states. Accordingly, implementation of a suggestedoperating schedule generated according to the system, method orapparatus described herein can result in actions that differ from moreheuristic approaches.

According to an example of the principles herein, the wholesaleelectricity market may include both the energy market and the regulationmarket, and the operating schedule generated may facilitateimplementation of the energy storage asset in both the energy market andthe regulation market. According to a principle of virtual partitioningdescribed herein, the operating schedule for the controller may beconfigured so that the energy customer may participate in both theenergy market and the regulation market concurrently the energy storageasset. In a non-limiting example, the operating schedule for thecontroller of the energy storage asset may specify that, during a giventime interval, a first portion of an available state of charge (SOC) ofthe energy storage asset may be used in the energy market and a secondportion of the available SOC of the energy storage asset may becommitted to the regulation market. The operating schedule generated forthe controller may be used to provide energy-related revenue for theenergy consumer based on both the energy market and the regulationmarket. The principles and implementations described above in connectionto FIG. 1 are also applicable to a system operating according to theprinciples of virtual partitioning.

In another non-limiting example, the apparatus of FIG. 3 can be used fordetermining an operating schedule of a controller of at least one energyasset operated by an energy customer of an electricity supplier, so asto generate energy-related revenue, over a time period T, associatedwith operation of the at least one energy asset according to theoperating schedule, wherein the energy-related revenue available to theenergy customer over the time period T is based at least in part on awholesale electricity market. The wholesale electricity market mayinclude an energy market. The energy-related revenue may also beavailable from a regulation market. The apparatus includes at least onecommunication interface, at least one memory to storeprocessor-executable instructions and a mathematical model for the atleast one energy asset, and at least one processing unit. Themathematical model facilitates a determination of the operating schedulefor the controller of the at least one energy asset based at least inpart on an operation characteristic of the at least one energy asset, aforecast wholesale electricity price associated with the energy market,and a regulation price associated with the regulation market. The atleast one processing unit is configured to determine the operatingschedule for the controller of the at least one energy asset using themathematical model by minimizing a net energy-related cost over the timeperiod T. The net-energy related cost is based at least in part on theduration of energy asset participation in the regulation market,electricity generation by the at least one energy storage asset, andelectricity consumption by the at least one energy asset (including theenergy storage asset). The energy-related revenue available to theenergy customer is based at least in part on the minimized netenergy-related cost. The operating schedule specifies, during a timeinterval within the time period T, a first portion of an availableoutput of the controller for use in the energy market and a secondportion of the available output of the controller for use for use in theregulation market. The at least one processing unit is also configuredto control the at least one communication interface to transmit to theenergy customer the operating schedule for the controller of the atleast one energy asset and/or controls the at least one memory so as tostore the determined operating schedule for the controller.

In this example, the available output of the controller is a charge rateof the at least one energy storage asset or a discharge rate of the atleast one energy storage asset. The net energy-related cost may bespecified as a difference between an electricity supply cost and aneconomic demand response revenue over the time period T. The operationcharacteristic of the at least one energy storage asset is a state ofcharge, a charge rate, a degree of non-linearity of charge rate adischarge rate, a degree of non-linearity of discharge rate, a roundtrip efficiency, or a degree of life reduction.

Energy Asset Modeling

To facilitate the mathematical optimization process for generating asuggested operating schedule for one or more energy assets according tovarious examples of the principles herein, a mathematical modelrepresenting an energy customer's energy asset(s) is formulated andemployed to simulate an “energy profile” for the asset(s).

In an example, the building asset model is an engineering-based model.Various examples and aspects of engineering-based building asset modelsthat are applicable to any of the apparatus, methods, and/or computerreadable media are described herein. An example engineering-based modeldescribed herein can be a two-node model that provides improvement inthermodynamic and HVAC predictions with automated parameter search andvalidation. The engineering-based model can generated from amathematical model that includes one or more terms to the physicalattributes of at least one component of the energy asset(s) themselvesas they relate to energy use and or generation, including electricityuse and/or electricity generation. An engineering-based model describedherein can be used to provide an improvement on a physical model,including a HVAC system model (such as a chiller model, AHU model,ice-storage model, etc.). In an example, the engineering-based modelincludes one or more equations and/or parameters that are based onphysical components and/or systems in the buildings. For example, anengineering-based model can be developed to take into account thethermodynamics of some components (e.g., using a thermodynamic equationfor a HVAC based on a heat capacity), the heat conduction or otherenergy exchange of the building asset based on a temperature differencebetween the inside and outside of the building (modeled as a circuitelement equation). A transfer function, as described, e.g., in Seem etal. (1989), “Transfer Functions for Efficient Calculation ofMultidimensional Transient Heat Transfer,” ASME J. Heat Transfer, Vol.111, pp. 5-12 (which is incorporated herein by reference in itsentirety), may be used to model the heat change behavior. An exampleengineering-based model described herein is applicable to a sensedbuildings (for which there is data on the operation characteristics ofits energy assets, including the operation characteristics of one ormore of its components).

Depending on the energy asset(s) operated by the energy customer, themathematical function(s) defining an asset model may represent a singleenergy asset or an aggregation of multiple energy assets operated by thecustomer. For purposes of the discussion herein, the term “asset model,”unless otherwise qualified, is used generally to denote a modelrepresenting either a single energy asset or an aggregation of multipleenergy assets.

To illustrate the general concept of an asset model, a model is firstconsidered for one or more energy assets that not only may be turned“on” or “off,” but that may be controlled at various “operating setpoints.” For example, consider the case of a “building asset,” e.g., oneor more buildings including a heating, ventilation and air conditioning(HVAC) system for temperature control, for which the customer may choosedifferent temperature set points at different times (e.g., thermostatsettings); accordingly, in this example, the temperature set pointsconstitute “operating set points” of the building asset. In thisexample, the magnitude of the operating set point may vary as a functionof time t, in a continuous or step-wise manner (e.g., Temp(t)=72 degreesF. for 9 PM<t<9 AM; Temp(t)=68 degrees F. for 9 AM<t<9 PM). In otherexamples of energy assets that merely may be turned “on” or “off,” themagnitude of the operating set point may be binary (i.e., on or off),but the respective on and off states may vary as a function of time t(e.g., over a given time period T).

Based on the notion of time-varying operating set points for energyassets, the term “operating schedule” as used herein refers to anoperating set point of one or more energy assets as a function of time,and is denoted by the notation SP(t):

-   -   SP(t)≡operating schedule for one or more energy assets.

The amount of energy used (and/or generated) by a particular asset orgroup of assets in a given time period T is referred to herein as an“energy profile.” In various implementations discussed herein, theenergy profile of one or more assets often depends at least in part on agiven operating schedule SP(t) for the asset(s) during the time periodT. For a fixed-load asset, the energy profile may not depend on a givenoperating schedule SP(t). Accordingly, an energy asset model specifiesone or more mathematical functions for calculating an energy profile(i.e., electricity use and/or electricity generation as a function oftime) for the asset(s), based on a proposed operating schedule for theasset(s) applied as an input to the model. The one or more functionsconstituting the asset model are denoted herein generally as F (and forsimplicity the term “function” when referring to F may be used in thesingular), and the model may be conceptually represented usingmathematical notation as:

F(SP(t))=EP(t),  Eq. 1

where the operating schedule SP(t) is an argument of the function F, andthe energy profile of the modeled asset(s) as a function of time isdenoted as EP(t). In a non-limiting example, EP(t) has units of MWh.FIG. 4 illustrates a simple block diagram representing the asset modelgiven by Eq. 1.

In various examples, the function(s) F defining a particular asset modelmay be relatively simple or arbitrarily complex functions of theargument SP(t) (e.g., the function(s) may involve one or more constants,have multiple terms with respective coefficients, include terms ofdifferent orders, include differential equations, etc.) to reflect howthe asset(s) consume or generate energy in response to the operatingschedule SP(t). In general, the particular form of a given function F,and/or the coefficients for different terms, may be based at least inpart on one or more physical attributes of the asset(s), and/or theenvironment in which the asset(s) is/are operated, which may impact theenergy profile of the asset(s) pursuant to the operating schedule. Morespecifically, depending on the type of energy asset(s) being modeled,the mathematical model may be formulated to accept other inputs (inaddition to the operating schedule SP(t)), and/or to accommodatevariable parameters of a given function F (e.g., via time-dependentcoefficients of different terms of the function), to facilitatecalculation of the energy profile EP(t) based on a proposed operatingschedule SP(t).

For example, in the case of the building asset discussed above, and/orother assets for which thermodynamic considerations are pertinent,various internal factors that may impact the asset's energy profile ingeneral (e.g., building occupancy; a presence of equipment such ascomputers and other instrumentation that may affect heating or coolingin an environment; thermal inertia due to insulation, buildingmaterials, windows; etc.) may be considered in the formulation of theform of the function F itself, and/or coefficients for different termsof the function F. In some examples discussed in further detail below,such as with a real-time building asset model, the function F may bedynamically adjusted based on an observation/measurement of actualenergy usage over time by the asset(s) pursuant to the updatingperformed during implementation of Kalman filtering. In some examplesdiscussed in further detail below, the function F may be dynamicallyadjusted based on observing actual energy usage over time by theasset(s) pursuant to control via a particular operating schedule (i.e.,coefficients of function terms initially may be estimated, andsubsequently adjusted over time based on real-time feedback fromcontrolled assets).

Similarly, the mathematical model for the asset(s) may be configured toconsider as an input to the model actual or forecast ambientenvironmental conditions (e.g., temperature, humidity, ambientlight/cloud cover, etc.) as a function of time, collectively denoted as“weather information” W(t), which may impact the energy profile of oneor more assets. In this case, the model may be conceptually representedas:

F(SP(t),W(t))=EP(t),  Eq. 2

where both the operating schedule SP(t) and the weather information W(t)are arguments of the function F. FIG. 5 illustrates a simple blockdiagram representing the asset model given by Eq. 2. It should beappreciated that, while weather information W(t) is noted above asproviding another possible input to the model in addition to theoperating schedule SP(t), in other examples one or more other inputs tothe model may be provided and considered as arguments to the function F(and accordingly taken into consideration in the function) for purposesof calculating an energy profile EP(t) for the asset(s).

In another example herein, the mathematical model for a system thatincludes a controllable asset, such as an energy storage asset and anassociated controller, or other component with a controller (e.g., aHVAC), may be configured to consider as an input to the model thecontrol vector for the controller as a function of time, denoted asu(t), which may impact the energy profile. In this case, the model maybe conceptually represented as:

F(u(t))=EP(t),  Eq. 3

where both the control vector of the controller is an argument of thefunction F. FIG. 6 illustrates a simple block diagram representing theasset model given by Eq. 3. It should be appreciated that, while thecontrol vector u(t) is noted above as providing input to the model, inother examples, one or more other inputs to the model may be providedand considered as arguments to the function F (and accordingly takeninto consideration in the function) for purposes of calculating anenergy profile EP(t) for the asset(s). An energy storage asset hereingenerally refers to an asset that can store a form of energy and releaseit as usable energy (or power) over time. Non-limiting examples ofenergy storage assets include batteries, ice units, compressed air,flywheel, heated liquids, and heated solids. Non-limiting examples ofbatteries include lithium ion batteries, lead-acid batteries, flowbatteries, or dry cell technology batteries.

In yet another example herein, the mathematical model for a system thatincludes controllable energy assets, including energy consuming assetssuch as but not limited to a building asset including a (HVAC) systemfor temperature control, or an energy storage asset and an associatedcontroller, may be configured to consider as an input to the model thecontrol vector for the controller as a function of time, denoted asu(t), and temperature dependent operating set points for the energyconsuming asset (its operating schedule). In this case, the model may beconceptually represented as:

F(u(t),SP(t))=EP(t),  Eq. 4

where both the control vector of the controller is an argument of thefunction F. FIG. 7 illustrates a simple block diagram representing theasset model given by Eq. 4. In an example, the control vector for acontroller, u(t)=C_(t)+D_(t), may be expressed as:

C _(t) =u _(1,t) *C/D _(max)

D _(t) =u _(2,t) *C/VD _(max)  Eq. 5

with the constraints that u_(1,t)*u_(2,t)=0 and 0≦u_(1,t), u_(2,t)≦1,where represents C/D_(max) the maximum charge rate or discharge ratecapacity of the controller in communication with the energy storageasset.

In yet another example herein, the mathematical model for a system thatincludes an energy consuming asset, such as but not limited to abuilding asset, and a controllable asset, such as but not limited to anenergy storage asset and an associated controller, or other componentwith a controller (e.g., a HVAC), may be configured to consider as aninput to the model the control vector for the controller as a functionof time, denoted as u(t), and temperature dependent operating set pointsfor the energy consuming asset (its operating schedule). FIG. 8illustrates a simple block diagram representing the asset model for suchas system according to the principles herein. In this case, the modelmay have outputs of the state of charge (SOC_(t)) of the energy storageasset as a function of time t, the return-air-temperature (RAT_(t))(including the zone temperature (T_(z))) as a function of time t (for,e.g., a HVAC or other similar equipment), and the energy profile as afunction of time (EP_(t)) of the energy consuming asset (e.g., somecomponents of the building asset). In another example, the output is azone temperature (T_(z)) as a function of time and the energy profile(EP_(t)). In yet another example, the output is a return-air-temperature(RAT_(t)), a zone temperature (T_(z)) and an energy profile (EP_(t)) asfunctions of time. In yet other examples, the output is a load as afunction of time. Other inputs to the system can be weather information(W(t)) and/or feedback from other energy assets in the system (V). Thismodel can be used, e.g., for co-optimization of an energy storage assetand an energy consuming asset for the energy market.

In an example according to a principle herein, once an appropriate assetmodel is established for a given energy asset or group of energy assets,different candidate operating schedules may be applied to the model tosimulate how the energy profile EP(t) of the asset(s) is affected as afunction of time, over a given time period T, by the different operatingschedules.

An example technique for facilitating determination of optimal operatingschedule for energy cost reduction and/or revenue generation fromwholesale electricity markets according to various examples disclosedherein is as follows. In this example, the system includes an energyconsuming asset, a controller of the energy storage asset, and acontrollable energy consuming asset. A plurality of first candidateoperating schedules is selected for the controller, and a plurality ofsecond candidate operating schedules is selected for the energyconsuming asset. Each second candidate operating schedule for the energyconsuming asset is different from the BAU operating schedule for theenergy consuming asset. The plurality of first and second candidateoperating schedules are successively applied to the mathematical modelto generate corresponding plurality of simulated energy profiles for theenergy storage asset and the energy consuming asset. A plurality ofprojected net energy-related costs to the energy customer are computed,where each projected net energy-related cost is computed based at leastin part on the representative CBL energy profile and the simulatedenergy profiles corresponding to the respective first and secondcandidate operating schedules and the forecast wholesale electricityprice. Respective ones of the first and second candidate operatingschedules corresponding to one simulated energy profile of the pluralityof simulated energy profiles that results in a minimum netenergy-related cost of the plurality of net energy-related costscalculated are selected as an optimal first operating schedule and anoptimal second operating schedule. That is, namely, this technique canbe implemented to simulate how energy assets consume/generateelectricity based on different candidate operating schedules for theasset(s), and to select a particular operating schedule that facilitatesa particular economic goal of the energy customer.

In another example, the operating schedules for the energy storage assetand energy consuming asset can be calculated in tandem based onminimizing the net energy-related costs (NEC), as discussed in greaterdetail below.

Operating Schedules and Constraints

In considering various operating schedules SP(t) that may be applied tothe asset model so as to simulate a corresponding energy profile EP (t),in some instances SP(t) may not be varied freely. Such limitations oncandidate operating schedules may be due at least in part to physicallimitations of the asset(s) being modeled (such as for anengineering-based energy asset model), and/or limitations on operationof the asset(s) dictated by the energy customer itself. For example, insome instances the customer may want to constrain the range in which themagnitude of SP(t) may be varied at any given time, and/or the customermay wish to designate particular periods of time (e.g., within the giventime period T of interest) during which particular values of SP(t)cannot be changed (or only changed in a limited manner).

For purposes of illustration, again consider a building asset with anHVAC system. The customer may specify that, in considering candidateoperating schedules SP(t) for the building asset, temperature set points(i.e., the magnitude of SP(t) in this example) must remain in a range offrom between 65 to 75 degrees F. in any proposed operating schedule;furthermore, the customer may dictate that during a certain time frame,the temperature set point may not exceed 70 degrees F. In general,magnitude and/or timing limitations placed on a candidate operatingschedule SP(t) for one or more modeled assets are referred to herein as“constraints” on the operating schedule.

The concept of candidate operating schedules for one or more modeledenergy assets subject to one or more “constraints” is denoted herein as:

SP(t)|_(Cstraints)≡operating schedule for one or more energy assetssubject to constraints

The “constraints” may also be set to impose limitations on stateoutputs, including the resulting temperature. The “constraints” on thestate outputs may impose limitations on ranges of values for the stateoutputs, such as but not limited to limiting the temperature of thebuilding asset to specific ranges. For example, the constraints imposedon the control inputs may be optimized so that the state outputs(including temperature) are maintained within specified ranges.

In an example, the system includes an energy storage asset, andconstraint may be placed on the allowed state of charge (SOC) of theenergy storage asset. For example, the constraint may be placed that theSOC does should not be allowed to fall below a minimal SOC value (i.e.,not too depleted) and/or that the SOC does should not be allowed to goabove a maximal SOC (i.e., not overly-charged).

Business-As-Usual (BAU) Conditions and Customer Baseline (CBL) EnergyProfiles

Once an appropriate asset model is established for a given energy assetor group of energy assets, a particular operating schedule of interestin some examples is referred to herein as a “typical” or“business-as-usual” (BAU) operating schedule (also referred to herein as“BAU conditions”), denoted as SP(t)_(BAU). In particular, “BAUconditions” refer to an operating schedule that an energy customer wouldtypically adopt for its energy asset(s), absent the incentive to reduceenergy costs and/or earn energy-related revenue from wholesaleelectricity markets. Again turning to the example of a building assetfor purposes of illustration, absent any incentive to change itsbehavior, during a summer season in which cooling is desired an energycustomer may typically set the thermostat (i.e., temperature set points)for the building asset at 72 degrees F. from 9 PM to 9 AM, and at 68degrees F. from 9 AM to 9 PM; this can be represented conceptually usingthe notation adopted herein as:

${{SP}(t)}_{BAU} = {\begin{Bmatrix}{72,} & {{9{PM}} < t < {9{AM}}} \\{68,} & {{9{AM}} < t < {9{PM}}}\end{Bmatrix}.}$

When a typical operating schedule SP(t)_(BAU) is applied to the assetmodel, the particular energy profile generated by the model is a specialcase referred to herein as a simulated “customer baseline” (CBL) energyprofile, denoted as CBL(t). Using the example relationship given in Eq.2 above (which includes consideration of weather information), thespecial case of a CBL energy profile may be conceptually representedmathematically as:

F(SP(t)_(BAU) ,W(t))=CBL(t),  Eq. 6

where the typical operating schedule SP (t)_(BAU) is an argument of thefunction F (in this example together with the weather information W(t)),and the CBL energy profile of the modeled asset(s) as a function of timeis denoted as CBL(t).

Although consideration of weather information W(t) is included in theexample above, it should be appreciated that the simulation of acustomer baseline (CBL) energy profile in other examples may notconsider weather information (as such information may not be relevant tothe energy profile of the asset(s) in question). It should also beappreciated that while the simulation of a CBL energy profile may beuseful for mathematical optimization techniques employed in someexamples to facilitate energy cost reduction and/or revenue generationfrom particular wholesale electricity markets (e.g., economic demandresponse “energy markets”), simulation of a CBL energy profile may notbe applicable or necessary in other examples to facilitate energy costreduction and/or revenue generation from wholesale electricity markets.

Objective Cost Functions and Optimal Control

For purposes of the present disclosure, an “objective cost function”specifies all energy-related costs and energy-related revenuesassociated with operating one or more modeled energy assets of an energycustomer so as to achieve a particular economic goal (an economic“objective”). In one aspect, an objective cost function incorporates thefunction(s) F representing the mathematical model for one or more energyassets, and specifies an energy customer's “net energy-related cost”(e.g., in dollars) associated with operating the modeled asset(s) over agiven time period T. The energy customer's net energy-related cost asgiven by the objective cost function is denoted herein as NEC$:

-   -   NEC$=net energy-related cost to operate one or more energy        assets.        As discussed in greater detail below, objective cost functions        providing a net energy-related cost NEC$ according to different        examples may have a variety of respective cost and revenue        terms, based at least in part on the types of asset(s) being        operated and the particular revenue-generation objective(s)        (e.g., the particular wholesale electricity market(s) from which        revenue is being sought).

In an example, the objective cost function, control inputs to theobjective cost function, and the state outputs can be continuousfunctions of time, which can form a basis for the optimal control (asopposed to parametric optimization). A parametric optimization may findcertain solutions for the PID that deliver an extremum solution butwhich is not the optimized solution.

For example, in some examples, the energy-related costs included in theobjective cost function may include “actual” energy-related costs (e.g.,retail electricity costs, wholesale electricity costs representingrevenue earned by the energy customer, etc.). In some examples, theenergy-related costs included in the objective cost functionadditionally or alternatively may include “indirect” energy-relatedcosts, such as convenience/comfort costs associated with the energycustomer's adoption of a suggested operating schedule different than theBAU operating schedule (the convenience/comfort cost represents an“indirect” cost associated with a change in the customer's behavior withrespect to operating its asset(s), based on the incentive of possibleenergy-related revenue from the wholesale electricity markets). In anexample, energy-related costs included in the objective cost functionmay include reliability costs associated with voltageNAR control in amicrogrid application. Similarly, an objective cost function may includeone or more terms specifying energy-related revenues corresponding toone or more wholesale electricity markets (e.g., “energy markets,”“synchronized reserve,” “regulation”).

To provide a preliminary illustration of concepts germane to anobjective cost function specifying a net energy-related cost NEC$, anexample relating to economic demand response revenue from the wholesaleelectricity “energy markets” is first considered. To this end, retailelectricity prices (i.e., what the energy customer pays a “utility” forelectricity usage) and wholesale electricity-related product pricesavailable to the energy customer respectively are denoted as:

-   -   Retail$(t)=price of electricity from a retail electricity        provider (“utility”); and    -   Wholesale$(t)=price of electricity-related product on applicable        wholesale electricity market,        where the retail electricity price Retail$(t) and the wholesale        electricity-related product price Wholesale$(t) may vary        independently of each other as a function of time. In an        example, the units of the retail electricity price Retail$(t)        and the wholesale electricity-related product price        Wholesale$(t) are $/MWh.

The wholesale price Wholesale$(t) can be dictated by (e.g., based atleast in part on) the “locational marginal price” (LMP) as a function oftime, as noted above (see Background section). However, depending on agiven wholesale electricity market and/or a particularelectricity-related product in question, it should be appreciated thatthe wholesale price Wholesale$(t) may be based on other and/oradditional factors. Also, practically speaking, wholesale prices are notcontinuous functions of time; rather, as discussed above, wholesaleprices based on the LMP may be calculated periodically at specifiednodes of the grid (e.g., every 5 minutes, every half-hour, every hour)depending on the particular market in which the energy customer isparticipating. Accordingly, it should be appreciated that Wholesale$(t)typically is a discrete function of time, with t having some periodicity(e.g., 5 minutes, 30 minutes, 60 minutes).

Given the notation above for retail and wholesale prices, the energycustomer's modeled retail electricity costs (or “supply costs”), foroperating one or more modeled electricity-consuming assets pursuant to aparticular operating schedule SP(t) applied to an asset model, isdenoted herein as Supply$(t), given by:

Supply$(t)=EP(t)*Retail$(t),  Eq. 7

wherein EP(t) is the energy profile of the modeled asset(s) (e.g., givenby any of Eqs. 1-4 above).

For the energy storage asset, the energy customer's “supply costs” forcharging the asset can be denoted herein as Supply$(t)_(ES), given by:

Supply$(t)_(ES) =EP(t)*Retail$(t),  Eq. 8

wherein EP(t) is the energy profile of the modeled energy storageasset(s). Since the energy profile for an energy storage asset can berepresented based on a charge rate (C_(t)) for a time step (t<7) overthe amount of time of charging (Δt), the supply costs can be expressedas:

Supply$(t)_(ES) =C _(t) *Δt*Retail$(t).  Eq. 9

The charge rate (C_(t)) may be the maximum charge rate of the energystorage asset, or a charge rate less than the maximum charge rate. Forexample, in different examples herein, the output of the controller maymodify the charge rate of the energy storage asset to values that areless than the maximum charge rate.

If the system includes an energy storage asset and an energy generatingasset, the total supply costs can be expressed, in a non-limitingexample, as the energy storage asset (Supply$(t)_(ES)) reduced by a costamount based on the amount of energy provided by the energy generatingasset (EG_(k)). In an example, the total supply costs can be expressedas:

Supply$O_(total)=(C _(k) −EG _(k))*Δt*Retail$(t).  Eq. 10

Supply costs may also apply to the system by virtue of an emissions costassociated with the operation of at least one of the energy assets. Thatis, the net-energy related cost can include a term based on theemissions costs (Emissions$) associated with, as non-limiting examples,the electricity consumption by an energy consuming asset or an emissionscost based on operation of an energy generating asset. The emissionscosts can be associated with greenhouse gas emissions during operationof the system. Non-limiting examples of such emissions are CO_(x)emissions (e.g., carbon monoxide and carbon dioxide emissions), NO_(x)emissions (e.g., nitrogen dioxide and related pollutant emissions),SO_(X) emissions (e.g., sulfur dioxide and related pollutant emissions),particulates, and methane (CH₄) and related pollutant emissions. In anexample, the contribution to the supply costs (Supply$(t)) based on theemissions costs (Emissions$) may be computed based on an economicbenefit, such as but not limited to a prorated amount of a penalty costleveled due to an amount of emissions, over a time period T, associatedwith the operation of the at least one energy asset. The penalty costmay be in staged amounts. For example, a first penalty may be imposedfor an amount of emissions above a first threshold value but below asecond threshold emissions value, and a second (higher) penalty may beimposed for an amount of emissions above the second threshold emissionsvalue. Such penalties may be leveled by a government agency (in acommand-and-control program, a regulation program, or a voluntaryprogram) or may be determined based on participation in anon-governmental voluntary emissions reduction program. In anotherexample, the contribution to the supply costs based on the emissionscosts may be computed based on a trading price of an emissions creditbased on an amount of emissions, such as but not limited to a tradingprice of a carbon credit based on CO_(x) emission (also an economicbenefit). For example, trading on financial markets may be performedunder international protocol (such as but not limited to the KyotoProtocol). As another example, trading may occur under a U.S. regionalemissions reduction program, such as but not limited to the RegionalGreenhouse Gas Initiative (RGGI).

The contribution to the (Supply$(t)) based on the emissions costs(Emissions$) may be computed based on base emissions cost for a minimallevel of operation of the energy asset(s), over time period T, and amarginal emissions cost per megawatt with each increase or decrease inmegawatt of operation. In an example, the marginal emissions cost can becomputed based on a change in the amount of emissions generated by anenergy generating asset that is in communication with the at least oneenergy asset, associated with the increase or decrease in megawatt ofoperation. As a non-limiting example, where the energy generating assetis a diesel generator, the emissions costs can include a termrepresenting the amount of emissions by virtue of use of diesel fuel. Inan example, the marginal emissions cost can be computed based on amarginal supplier emissions cost associated with a change in electricityusage by the energy customer associated with the increase or decrease inmegawatt of operation. The marginal supplier emissions cost can bequantified based on an amount of emissions from an electricitygenerating facility of the electricity supplier per megawatt-hour ofelectricity supplied. The marginal supplier emissions cost can verybased on the type of electricity generation facility. As an example, theemissions from a coal-based electricity generation facility can behigher than the emissions from a hydro-power electricity generationfacility or a gas-powered electricity generation facility. In thisexample, the marginal supplier emissions cost for a coal-basedelectricity generation facility for each incremental increase ordecrease in megawatt-hour of electricity supplied can be higher than themarginal supplier emissions cost for a hydro-power electricitygeneration facility or the marginal supplier emissions cost for agas-powered electricity generation facility.

The emissions costs (Emissions$) can be computed based on a change inthe energy customer's behavior in reducing its electricity usage basedon an operation of the at least one energy asset. For example, theemissions cost can be specified as a cost function based at least inpart on at least one difference between the energy customer's behaviorin reducing its usage of the energy asset(s) and their BAU operatingschedule. In an example, the energy asset(s) can include at least onebuilding having a variable internal temperature controlled by a HVACsystem, and the emissions cost is based on a difference between acandidate temperature set point for the HVAC system as a function oftime and the BAU temperature set point. The emissions cost can becomputed as proportional to the magnitude of deviation of thetemperature of the building from a BAU temperature set point.

Supply costs may also apply to the system by virtue of lifetime and/orreplacement costs for electricity generation and/or energy storageassets, such as but not limited to due to the reduction in life of theenergy storage asset. An energy storage asset may have a limited lifedepending on its rating of expected charge/discharge cycles. A portionof the costs associated with ultimately replacing an energy storageasset at the end of its lifetime may be included in the supply costsbased on the number of charge/discharge cycles it is expected to undergowhen implemented in an energy market and/or a regulation market asdescribed herein. The lifetime reduction supply costs may also depend onthe number of kWh is used in each charge or discharge cycle, and/or forwhat length of time the energy storage asset is used in a market(energy, regulation, etc.). For example, the contribution to the supplycosts based on the replacement cost (Replacement$) may be computedaccording to the expression:

Supply$(t)_(LIFE)=Replacement$/n  Eq. 11

where n represents an effective number of charge/discharge cycles. Theeffective number of charge/discharge cycles can depend on the number ofcycles the asset is expected to undergo when implemented in an energymarket and/or a regulation market, the number of kWh is used in eachcharge or discharge cycle, and/or for what length of time the energystorage asset is used in a given market. This lifetime supply cost wouldbe additive to any of the expressions for supply costs described hereinfor a system that includes an energy storage asset.

Non-limiting examples of other energy-related costs applicable to thesystems, methods and apparatus described herein include fuel costs torun one or more electricity generation assets, operation and/ormaintenance costs that may be associated with electricity generationand/or energy storage assets, tariffs that can be leveled in theindustry, demand charges that can be leveled at times of peak energyusage, etc.

With respect to economic demand response revenue from the wholesaleelectricity energy markets, in the present example it is presumed thatthe energy customer is amenable to operating its energy asset(s)pursuant to a candidate operating schedule that is different than its“typical operating schedule” or BAU conditions (i.e., SP(t)_(BAU)), suchthat the energy profile EP (t) of the asset(s) will be on average lowerthan the customer baseline CBL(t) (see Eq. 6 and related descriptionabove). Altering the energy profile of the asset(s) with respect to thecustomer baseline, pursuant to a change in behavior represented by acandidate operating schedule different than BAU conditions, provides thesource of opportunity for generating economic demand response revenuefrom the wholesale electricity energy markets.

Accordingly, a wholesale electricity energy market “demand responserevenue,” denoted herein as DR$(t)_(EM), is given generally by:

DR$(t)_(EM)=max{0,[(CBL(t)−EP(t))*Wholesale$(t)]}  Eq. 12

In an example, the DR$(t) represents the net difference between actualnet metered load and the BAU load. The participation of any component ofthe energy asset in the energy market, regulation market or spinningreserve market is included in the computation of DR$(t) to the extentthey affect the value of the metered load. In addition, the energygenerated by any energy generating asset that is part of the energyasset may also be included in the computation of DR$(t) to the extentthis behind-the-meter generated energy affects the value of the meteredload.

For an energy storage asset in an energy market, a demand responserevenue may be denoted herein as DR$(t)_(ES), is given generally by:

DR$(t)_(ES)=(0−(—(D _(t)))*Δt*Wholesale$(t).  Eq. 13

As described herein, a system that includes an energy storage asset canparticipate in both an energy market (at a price of Wholesale$(t)) andin a regulation market (at a price of regulation$(t)). In this example,the demand response revenue may be computed herein as DR$(t)_(ES),denoted by:

DR$(t)_(ES)=(εD _(t))*Δt*Wholesale$(t)+(γD _(t))*Δt*regulation$(t)  Eq.14

where D_(t) denotes the discharge rate of the energy storage asset at atime step. Where the system participates in the energy market and theregulation market at different points in time during overall time periodT, both multipliers of the discharge rate, ε and γ, may be equal to 1.In different examples herein, the output of the controller may modifythe discharge rate of the energy storage asset to values that are lessthan the maximum discharge rate. Using the principles of virtualpartitioning described herein, by apportioning an output of thecontroller in communication with the energy storage asset, a portion ofthe discharge rate may be directed to the regulation market and anotherportion directed to the energy market during a given time step. As anon-limiting example, the operating schedule determined as describedherein may cause the controller to discharge the energy storage asset ata discharge rate of εD_(t) to the energy market, while concurrentlyrespond to the regulation market at a discharge rate of γD_(t) alongshorter timescales (such as but not limited to at 2-second intervals orminute-by-minute time intervals). Here, the constraint on the values maybe ε+γ≦1 if D_(t) represents the maximum discharge rate of the energystorage asset.

In a non-limiting example, where the regulation price is not based onthe discharge rate, but rather depends only on the time period ofcommitment of the energy storage asset to the regulation market, thedemand response revenue may be computed as:

DR$(t)_(ES)=(εD _(t))*Δt*Wholesale$(t)+regulation$(t)*Δt  Eq. 15

In another example, the demand response revenue for a system thatincludes an energy storage asset and an energy generating assetparticipating in an energy market may be computed as:

DR$(t)_(ES+EG)=(D _(t)))*Δt*Wholesale$(t)+(E _(EG))*Wholesale$(t)  Eq.16

where D_(t) denotes the discharge rate of the energy storage asset at atime step and E_(EG) denotes the energy provided by the energygenerating asset.

According to the principles described herein, a demand response revenuemay also be generated for a system that includes an energy storage assetand an energy generating asset participating in both an energy marketand a regulation market.

Based on the equations for supply costs and demand response above, anexample of an objective cost function to provide a net energy-relatedcost NEC$ over a given time period T for operating the modeled asset(s),considering both retail electricity supply costs and demand responserevenue can be computed based on the expression:

$\begin{matrix}{{{NEC}\; \$} = {\sum\limits_{t}^{T}{\left( {{{Supply}\mspace{11mu} {\$ (t)}} - {{DR}\; {\$ (t)}}} \right).}}} & {{Eq}.\mspace{14mu} 17}\end{matrix}$

In one example, an objective cost function as exemplified by Eq. 17 maybe provided to an optimizer (a particularly-programmed processor, alsoreferred to as a “solver”; such as processor unit 13 of FIG. 2) thatimplements a mathematical optimization process to determine a suggestedoperating schedule for the energy asset(s) over the time period T thatminimizes the net energy-related cost NEC$. Accordingly, the optimizersolves for:

$\begin{matrix}{{Min}\left\lbrack {\sum\limits_{t}^{T}\left( {{{Supply}\; {\$ (t)}} - {{DR}\; {\$ (t)}}} \right)} \right\rbrack} & {{Eq}.\mspace{14mu} 18}\end{matrix}$

By substituting the pertinent equations for supply costs and demandresponse (which depends on the modeling of the energy assets in a givensystem) back into Eq. 18, the various informational inputs provided tothe optimizer may be readily ascertained. Various examples and aspectsof engineering-based building asset models are described herein that areapplicable to any of the apparatus, methods, and/or computer readablemedia described herein for solving an objective cost function using theoptimizer.

As a non-limiting example, for a system that is participating in theenergy market, the various informational inputs provided to theoptimizer may be readily ascertained as follows:

$\begin{matrix}{{{Min}\left\lbrack {\sum\limits_{t}^{T}\left\{ {\left( {{{EP}(t)}*{Retail}\; {\$ (t)}} \right) - \left( {\max \left\{ {0,\left\lbrack {\left( {{C\; B\; {L(t)}} - {{EP}(t)}} \right)*{Wholesale}\; {\$ (t)}} \right\rbrack} \right\}} \right)} \right\}} \right\rbrack},} & {{Eq}.\mspace{14mu} 19}\end{matrix}$

where from Eq. 2

P(t)=F(SP(t)|constraints,W(t)),

and from Eq. 6

CBL(t)=F(SP(t)_(BAU) ,W(t)),

where again it is presumed for purposes of illustration that weatherinformation W(t) is relevant in the present example. From the foregoing,it may be seen that one or more of the following inputs may be providedto the optimizer in various examples:

-   -   F—one or more functions defining the mathematical model for the        energy asset(s);    -   SP(t)_(BAU)—BAU or “typical” operating schedule for the energy        asset(s);    -   Constraints—any timing and/or magnitude constraints placed on        candidate operating schedules for the energy asset(s);    -   W(t)—weather information as a function of time (if appropriate        given the type of energy asset(s) being operated);    -   u(t)—control vector for the controller in communication with the        energy storage asset;    -   Retail$(t)—retail price of electricity as a function of time;    -   Wholesale$(t)—wholesale price of electricity-related product as        a function of time;    -   Regulation$(t)—regulation price in regulation market as a        function of time; and    -   NEC$—the objective cost function describing the energy        customer's net energy-related cost associated with operating the        modeled energy asset(s).        Based on the foregoing inputs, the optimizer solves Eq. 19 by        finding an “optimal” operating schedule for the energy asset(s),        denoted herein as SP(t)_(opt), that minimizes the net        energy-related cost NEC$ to the energy customer:

SP(t)_(opt)=“optimal” or suggested operating schedule for one or moreenergy assets

In various implementations described herein, the optimizer may receiveone or more inputs, including but not limited to, the weatherinformation W(t), the retail electricity price Retail$(t), and thewholesale price of the electricity-related product Wholesale$(t) (andthe regulation price (regulation$(t))) as forecasted values providedfrom a third-party source, for the time period T over which theoptimization is being performed.

While a given optimizer in a particular implementation may employvarious proprietary techniques to solve for the minimization of anobjective cost function according to various examples of the principlesherein, conceptually the optimization process may be generallyunderstood as follows. In various implementations discussed herein, theoptimizer generates the operating schedule using the model of the systemthrough an optimal control procedure. In the various exampleimplementations, the optimizer determines an optimal operating scheduleover the defined time period (T) by optimizing an objective costfunction. For example, the optimizer can be implemented to determine theoperating schedule that generates the energy-related revenue byminimizing a function representing the net energy-related costs of thesystem over the time period (T). The net energy-related costs can becomputed based on the supply costs and the demand response revenue asdescribed herein, including in Eqts. 1-19 above. The optimizer optimizesthe objective cost function over the entire defined time period (T) togenerate the operating schedule. The generated operating schedule caninclude suggestions, for different specific time intervals within theoverall time period T, for when the controller can be used to implementthe energy storage asset in the energy market, in the regulation market,or in both the energy market and regulation market (through dynamicpartitioning).

In a non-limiting example of an implementation of the optimizer, somenumber N of candidate operating schedules SP(t)|_(Constraints) for themodeled asset(s) (together with weather information W(t), if appropriatebased on a given objective function) can be successively applied to theasset model given by the function(s) F to generate simulated energyprofiles EP(t) corresponding to the candidate operating schedules (seeEqs. 1-4). A net energy-related cost NEC$ given by the objective costfunction is calculated for each such simulated energy profile EP(t) (seeEq. 17), and the candidate operating schedule that minimizes theobjective cost function (i.e., the “optimal” operating scheduleSP(t)_(opt) that minimizes the net energy-related cost NEC$) is selectedas the suggested operating schedule to be provided to the energycustomer.

As noted earlier, the example above in connection with the objectivecost function of Eq. 17 is based on actual energy-related costs (e.g.,retail electricity cost) Supply$(t). In other examples, theenergy-related costs included in a given objective cost functionadditionally or alternatively may include “indirect” energy-relatedcosts, such as “convenience/comfort” costs associated with the energycustomer's adoption of a suggested operating schedule SP(t)_(opt)different than its typical operating schedule SP(t)_(BAU). In one aspectof such examples, a convenience/comfort cost represents an “indirect”cost in that it does not necessarily relate to actual energy-relatedexpenditures, but rather attributes some cost (e.g., in dollars)relating to a change in the customer's behavior with respect tooperating its asset(s), based on the incentive of possibleenergy-related revenue from the wholesale electricity markets.

Accordingly, in some examples, an alternative objective cost functionsimilar to that shown in Eq. 17 may be given as:

$\begin{matrix}{{{N\; E\; C\; \$} = {\sum\limits_{t}^{T}\left( {{{Comfort}\; {\$ (t)}} + {{Supply}\; {\$ (t)}} - {{DR}\; {\$ (t)}}} \right)}},} & {{Eq}.\mspace{14mu} 20}\end{matrix}$

where Comfort$(t) represents a convenience/comfort cost associated witha change in the energy customer's behavior with respect to operating itsasset(s). In an example where the energy-related costs included in theobjective cost function include reliability costs, they would beincluded in the computation (such as in Eq. 12) similarly to theComfort$(t).

A convenience/comfort cost Comfort$(t) may be defined in any of avariety of manners according to different examples. For example, in oneimplementation, a convenience/comfort cost may be based at least in parton a difference (e.g., a “mathematical distance”) between a givencandidate operating schedule and the typical operating schedule (BAUconditions) for the modeled asset(s)—e.g., the greater the differencebetween the candidate operating schedule and the typical operatingschedule, the higher the convenience/comfort cost (there may be moreinconvenience/discomfort attributed to adopting a “larger” change inbehavior). This may be conceptually represented by:

Comfort$(t)=G SP(t)|_(Constraints) —SP(t)_(BAU)|],  Eq. 21

where G specifies some function of the absolute value of the“difference” between a candidate operating schedule (e.g., in a giveniteration of the optimization implemented by the optimizer) and thetypical operating schedule.

To provide an example of how Eqs. 20 and 21 may be employed in anoptimization process to determine a suggested operating scheduleSP(t)_(opt) for an energy customer according to one example, againconsider a building asset operated by the energy customer, for which agiven operating schedule SP(t) is constituted by a temperature set pointas a function of time. If T(t)_(BAU) represents the temperature setpoints constituting a typical operating schedule, andT(t)|_(Constraints) represents different temperature set pointsconstituting a candidate operating schedule that may be adopted tofacilitate energy-cost reduction and/or revenue generation, theconvenience/comfort cost Comfort$(t) in this example may be defined as a“temperature set point deviation” T_(dev)(t), according to:

Comfort$(t)≡T _(dev)(t)=A(|T(t)|_(Constraints)−(t)_(BAU)|),  Eq. 22

where A is a constant that converts temperature units to cost units(e.g., degrees F. to dollars). In an example, A may be adjustable foreach individual time step, so A may be represented as a vector. Eq. 22specifies that there is a greater “indirect” cost associated withcandidate operating schedules having temperature set points that deviatemore significantly from the typical temperature set points (albeitwithin the constraints provided by the energy customer). In this manner,as part of the optimization process, potential revenue from thewholesale electricity markets may be “tempered” to some extent by aperceived cost, included in the objective cost function (see Eq. 20),that is associated with the inconvenience/discomfort of deviatingsignificantly from the typical operating schedule.

In the example above, although the multiplier A in Eq. 22 is discussedas a conversion constant, it should be appreciated that in otherexamples A may be an arbitrary function having as an argument theabsolute value of the difference between a candidate operating scheduleand the typical operating schedule as a function of time. Moregenerally, it should be appreciated that a convenience/comfort costComfort$(t) is not limited to the “temperature-related” example providedabove in connection with a building asset, and that other formulationsof a convenience/comfort cost as part of an objective function arepossible according to various examples of the principles herein.

In yet other examples of objective cost functions, different cost andrevenue terms of a given objective cost function may includecorresponding “weighting factors” (e.g., specified by the energycustomer), so as to ascribe a relative importance to the energy customerof the respective terms of the objective cost function in arriving at asuggested operating schedule SP(t)_(opt). For example, in someinstances, an energy customer may want to emphasize the importance ofincreasing prospective demand response revenue DR$(t) vis a visdecreasing supply costs Supply$(t) in solving the optimization problemto arrive at a suggested operating schedule; similarly, in otherinstances, an energy customer may want to emphasize convenience/comfortcosts Comfort$(t) vis a vis increasing prospective demand responserevenue DR$(t) in solving the optimization problem to arrive at asuggested operating schedule. The ability of an energy customer totailor a given objective cost function according to weighting factorsfor respective terms of the objective cost function provides an“elasticity” to the optimization process. Using the objective costfunction given in Eq. 20 above as an example, in one example suchweighting factors may be included in the specification of an objectivecost function as respective term multipliers:

$\begin{matrix}{{{N\; E\; C\; \$} = {\sum\limits_{t}^{T}\left\lbrack {\left( {\alpha*{Comfort}\; {\$ (t)}} \right) + \left( {\beta*{Supply}\; {\$ (t)}} \right) - \left( {\gamma*{DR}\; {\$ (t)}} \right)} \right\rbrack}},} & {{Eq}.\mspace{14mu} 23}\end{matrix}$

where α, β, and γ constitute the weighting factors. In an example,α+β+γ=1. In another example, α+β+γ≠1.

In another example, the value of a weighting factor may differ atvarious points during the day. For example, if it is preferred that theComfort$(t) takes a bigger part in the objective cost functioncomputation at certain points during the day, the factor a may beincreased relative to the other weighting factors.

In an example, the comfort cost is attributed to a change in the energycustomer's behavior in adopting the operating schedule, over the timeperiod T, for the at least one energy asset in lieu of the BAU operatingschedule. The comfort cost can be specified as a cost function based atleast in part on at least one difference between the operating scheduleand the BAU operating schedule. For example, the comfort cost can bebased at least in part on a difference between the candidate temperatureset point and the BAU temperature set point. In an example where theenergy asset is a building, the change in the energy customer's behaviorcan be monitored based on a difference in a return-air-temperature of asection of the building as compared to a return-air-temperature with theBAU operating schedule. In an example, the comfort cost can be computedas proportional to a magnitude of deviation a temperature of thebuilding from a BAU temperature set point. In another example, thecomfort cost can be computed as proportional to a magnitude of deviationa humidity of the building from a BAU humidity set point.

In an example implementation, the operating schedule can be generatedthrough applying an optimization using a net-energy related costfunction based only on the energy market. The result of the optimizationcan be used to provide recommendation for time intervals for the energycustomer to participate in the energy market, regulation market, or boththe energy market and the regulation market. For example, based on theresults of the optimization, the operating schedule may determine thatany excess charge/discharge capacity of the controller of the energystorage system may be committed to the regulation market on anhour-by-hour basis. For example, it can be determined that the anyexcess charge/discharge capacity of the controller may be committed tothe regulation market during the first 15 time intervals. Theoptimization may make such a determination depending on whether theforecast regulation price in the regulation market in this time intervaloffers opportunity for energy-related revenue during this time intervalor if considered in the context of the global optimization over timeperiod T. In an example, such a determination may be made depending onwhether the SOC of the energy storage asset is feasible for its use inthe regulation market. For example, it may be preferable for the energystorage asset to be near around a 50% SOC for it to be applicable to theregulation market. In addition, if it is decided to commit the energystorage asset to the regulation market for a time interval, e.g., forone or more 1-hour time intervals, the optimization described herein maybe re-performed based on the new input state of the system. Such newinputs can include the state of charge of the energy storage asset afterits commitment to the regulation market ends. In another non-limitingexample, the optimization may evaluate different SOC initial inputs toassess whether “recovery” from the regulation market is feasible forlater participation in the energy market.

In an example, a predetermined threshold value of wholesale electricityprice can be set at which it is decided that the excess charge/dischargecapacity of the controller will be committed to the regulation market.Based on the results of the optimization, a predetermined thresholdvalue of the LMP price, indicated by the dashed horizontal line, may beset. In addition, it may be determined that the first time interval ofcharging the energy storage asset occurs during the time period that Tcoincides with the time interval during which the forecast wholesaleelectricity price falls below the predetermined threshold value. It mayalso be determined in the operating schedule that a second time intervalof discharging the energy storage asset occurs coincides with a timeinterval during which the forecast wholesale electricity price exceedthe predetermined threshold value.

While the discussion above of example objective cost functions andoptimization of same to generate suggested operating schedules forenergy assets has been based at least in part on economic demandresponse revenue from wholesale electricity energy markets (and in someparticular examples involving building assets), it should be appreciatedthat the disclosure is not limited in this respect; namely, according toother examples, objective cost functions may be formulated and optimizedto achieve a wide variety of energy-related objectives associated withdifferent types of energy assets and revenue generation opportunitiesfrom wholesale electricity markets. For example, computation based onrevenue from the regulation market has also been described herein above,and optimization based on the wholesale price and the regulation priceare described herein below. In other examples, the principles herein canbe applied to other markets, such as the spinning reserve market.

Generating An Operating Schedule For Deriving Energy-Related Revenue

As discussed above, the output of an optimization process to minimize anenergy customer's net energy-related cost NEC$ (e.g., as specified by anobjective cost function) is typically provided as a suggested operatingschedule SP(t)_(opt) for one or more energy assets. Generally speaking,the suggested operating schedule SP(t)_(opt) may comprise one or moreset point values as a function of time that take into consideration allof the energy customer's modeled and controllable energy assets.

For example, in some instances involving multiple individually modeledand controllable energy assets, the suggested operating scheduleSP(t)_(opt) may comprise multiple time-varying control signalsrespectively provided to corresponding controllers for the differentenergy assets. In other cases, the energy customer may have an energymanagement system (EMS) (or where applicable, building management system(BMS), or a building automation system (BAS)) that oversees control ofmultiple energy assets, and the suggested operating schedule SP(t)_(opt)may comprise a single control signal provided to the energy customer'sEMS, which EMS in turn processes/interprets the single control signalrepresenting the suggested operating schedule SP(t)_(opt) to controlrespective energy assets. In an example, the EMS has pre-defined “demandlevels” that control the power consumption of various assets, includingmodifying the setpoint for the building zone air temperature, modifyinglighting levels, and modifying variable frequency and/or variable speedof components (such as but not limited to modifying the frequency and/orspeed of a motor, a fan, any other ventilation equipment, and/or apump). In an example, modifying the setpoint for the building zone airtemperature results in relaxing zone temperature setpoint. In anexample, modifying lighting levels limits lighting levels to certainpredesignated settings. In an example, modifying variable frequencyand/or variable speed limits the variable frequency and/or variablespeed to certain predesignated settings.

In examples in which the energy customer normally operates its energyasset(s) according to a typical operating schedule SP(t)_(BAU) (absentany economic incentive to change its energy-related behavior), thesuggested operating schedule SP(t)_(opt) may be conveyed to the energycustomer in the form of one or more “bias signals,” denoted herein byBias(t). In particular, one or more bias signals Bias(t) may represent adifference between the suggested operating schedule and the typicaloperating schedule as a function of time, according to:

Bias(t)=SP(t)_(opt) −SP(t)_(BAU).  Eq. 24

Eq. 24 applies in certain cases. In a more general case, the Biasoffsets the “demand level” over components of the energy asset. The biassignal (sent to a EMS) may cause controllers not only to act onindividual zone setpoints, but also may take other actions such as butnot limited to load sheddings (including shutting off non-essentialloads) changing lighting levels (including dimming lights), andmodifying the variable frequency and/or variable speed of certaincomponents (including motors, fans or other types of ventilationequipment, and pumps). Reducing the variable frequency and/or variablespeed of certain components may be implemented to reduce load.

In an example, in response to the signal sent to the EMS, the EMS maymake changes to operation settings of components of the energy asset(i.e., modulate the operation parameters of components). Non-limitingexamples include adjusting zone setpoints, dimming lights, shedding nonessential loads, reducing frequency and/or speed of variable components(including any motors, any fans, any other ventilation equipment, or anypumps).

Engineering-based Models For Modeling Energy Assets

According to the principles herein, the engineering-based modelingapproach can be used to facilitate modeling of energy assets. In anexample, one or more buildings can be modeled as energy asset.

Engineering-based models are described that can be implemented toprovide a dynamic simulation model of an energy asset that is adaptiveto physical changes in the energy asset.

For example, an engineering-based model according to the principlesdescribed herein can be developed as a physical building model withvarious numbers of input model parameters. A physical building model canbe implemented to output at least one of a BAU customer load profile anda temperature profile. The results of the dynamic simulation based onthe engineering-based model can be introduced to an optimization that isrun to determine an optimal suggested operating schedule for the energyassets being modeled, including the and controllable energy assetresources of the system.

Non-limiting example attributes of an engineering-based model accordingto the principles described herein are as follows.

In an example, a physical building model can be computationallyintensive, and can be time-consuming as a result: Input to the physicalbuilding model may require review of mechanical and architecturaldrawings of the building, including as-built construction drawings anddetailed site surveys to calculate parameters such as area and mass. Aphysical model may require manual tuning of area and mass whenparameters in the estimation. Parameters such as the number of people(building occupancy) and equipment also may require manual tuning Afixed load approximation may be required. The model may require modelvalidation for curve fitting to actual metered load. Some physicalmodels also may require multiple input data monitoring.

In an example, the robustness of the physical building model may affectaccuracy: The inertia of the thermodynamics (a time lag in response)should be unaccounted for. The physical model may be a single nodemodel. The physical model is configured such that the zone temperature(T_(z)) setpoint is achieved and maintained. FIG. 9 describes attributesof non-limiting example implementations of a day-ahead engineering-basedmodel, a real-time engineering-based model, and a day-afterengineering-based model.

In an example, the physical building model according to the principlesdescribed herein may be applicable to a building portfolio that includessensed buildings, i.e., buildings with some amount of data availablefrom monitoring of building performance and/or buildings for which someamount of data can be inferred (i.e., estimated or projected). While aphysical building model may perform well, an engineering-based buildingmodeling may provide enhanced, faster performance.

An example engineering-based model (EBM) can be used to increase thespeed of implementation with accuracy within or better than industrystandards. The EBM model can be a single-node model or a two-node model.In an example, the EBM models may bring together the effects ofthermodynamic properties of the building energy assets (includingradiation, conduction, convection and inertia) with the operationcharacteristics of controllable energy assets, such as but not limitedto the mechanical or HVAC systems. In an example, goals of the EBM areimprovement in thermodynamic and HVAC predictions with automated modelparameter search and validation. This EBM model may also provide animprovement on an HVAC system model (chiller model, AHU model,ice-storage model, etc.). The EBM can be used for all building types,including sensed buildings.

The EBM model according to the principles herein can be a data-drivenrepresentation of the dynamics and operation of a building asset.

The non-limiting examples of EBM models and implementation are describedin FIG. 9. The day-ahead EBM model for a system can be based on asemi-non linear regression and at least one other model term that is aphysical model of the thermodynamic property of the at least one energyasset and/or a physical model of an operation characteristics of one ormore controllable energy assets of the system. The day-ahead EBM can betrained using past building performance data. A real-time EBM model canbe based on a KF update to a linear regression and at least one modelterm that is a physical model of the thermodynamic property of the atleast one energy asset and/or a physical model of an operationcharacteristics of one or more controllable energy assets of the system.Real-time feedback can be used to update model parameter(s), such as butnot limited to using Kalman Filtering. The day-after EBM model can bebased on a highly non linear regression and at least one term that is aphysical model of the thermodynamic property of the at least one energyasset and/or a physical model of an operation characteristics of one ormore controllable energy assets of the system. The day-after EBM modelcan be fit to previous performance data when gathered when the energyassets were operated according to the suggested operating schedule.

Engineering-based models are described herein that can be implemented toprovide day-ahead energy asset models, the real-time energy assetmodels, or day-after energy asset models. The day-ahead energy assetmodels can be used to generate a suggested operating schedule, prior toa time period T, that can be used to operate the at least one energyasset to facilitate generation of the energy-related revenue. Thereal-time energy asset models can be used to generate updated operatingschedules during operation of the energy asset according to thesuggested operating schedule during time period T. The day-after modelcan be used to model the operation characteristics of the energy assetafter time period T.

Terms Of The Engineering-based Models

In a non-limiting example, the dynamic simulation model of the at leastone energy asset according to the principles described herein includesat least one term that is a physical model of a thermodynamic propertyof the at least one energy asset. For example, the energy asset is abuilding asset, and the term is derived by solving a steady-stateequation to provide a heat balance equation at each node in the system.

FIG. 10 shows a building asset that is modeled based on a lumpedelements thermodynamic approximation. Each of the four nodes (21-a,21-b, 21-c, 21-d) in the approximation models the heat balance at asection of the building asset. For example, nodes 21-a and 21-c mayrelate to an inside wall and an outside wall, respectively, of thebuilding asset. Nodes 21-b and 21-d may relate to the floor and ceilingof the building asset, respectively. The R_(i) terms and respectivecircuit elements approximate a thermal resistivity at the portion of thebuilding asset. The C_(i) terms and respective circuit elementsapproximate a heat capacity at the portion of the building asset. TheQ_(i) terms indicate heat flow.

The state-space equation in Eq. 25 is solved to model the zonetemperature (Tz) for the optimization.

$\begin{matrix}{{\frac{x}{\tau} = {{Ax} + {Bu}}}{y = {{c^{T}x} + {d^{T}u}}}} & {{Eq}.\mspace{14mu} 25}\end{matrix}$

Solving the state-space equation requires solving over a four-term state(x) and nine inputs (u). As a non-limiting example, a transfer functioncould be developed to solve the state-space equation.

FIGS. 11A-11E show the effective element approximation at eachindividual node (21-a to 21-d). Each node is centered at the wall,floor, ceiling, or other similar boundary structure of the buildingasset. The requirement for continuity of the heat balance at the centerpoint (shown in FIG. 11C) serves as a constraint on the value of thecoefficients of the circuit element approximation. Specifically, thecontinuity at the mid-point (in FIG. 11C) required for heat balanceimposes constraints on the relationship and values of the effectiveelements of the approximation. Based on the state-space functioncoefficient, and the constraints on the effective elements in FIGS.11A-11E, the coefficients A,B, C and D of Eq. 25 can be determined usingmatrices derived by solving the heat balance equations at each node orstate. FIGS. 12A and 12B show a non-limiting example of the matrix formsof A, B, C, D based on the R's and C's of the effective elementsapproximation, which can be used to solver for the R's and C's using thehistorical data.

An initial stage in the computation is to perform a parameter search.The parameter search determines the number and type of parameters to beused in the computation to reach an error value below a desired value.The heat balance equation is solved to provide a solution for the zonetemperature. To solve the heat balance equation, Eq. 26 is solved forheat capacity C_(z) over the effective element approximation at eachindividual node:

$\begin{matrix}{{{Cz}\frac{T_{z}}{t}} = {{Q_{sh}(t)} - {Q_{{c\; 1g},{HVAC}}(t)}}} & {{Eq}.\mspace{14mu} 26}\end{matrix}$

The R's and C's elements of the effective elements model are solvedusing the historical data, i.e., data representative of the operationcharacteristics and the thermodynamic properties of the energy assets.For example, the R's and C's can be solved based on historical data whenheat balance is achieved, i.e., when the state space equation heat gainload (Qsh) equals the cooling heat term (Qclg) (Qsh=Qclg). As anon-limiting example, an algorithm to determine the R's and the C's ofthe thermodynamic properties model (such as but not limited to a geneticalgorithm (GA)) can be performed to identify the parameters sufficientfor computing a solution with a desired error in measurement.

As a non-limiting example, the R's and C's can be solved using atransfer function, such as but not limited to the transfer functiondeveloped by Seem et al., but modified to be applicable to the systemsdescribed herein (where n=4). As a non-limiting example, the transferfunction can be determined based on Eq. 27:

$\begin{matrix}{Q_{w,k} = {{\sum\limits_{j = 0}^{2}{S_{w,j}u_{w,{k - j}}}} - {\sum\limits_{j = 1}^{2}{e_{w,j}Q_{w,{k - j}}}}}} & {{Eq}.\mspace{14mu} 27}\end{matrix}$

A solution can be found for C_(z) when the cooling system is set to“off” (i.e., Qclg=0). In the computation to perform the optimization,T_(z) can be constrained when solving for optimal Qsh for loadreduction. Tz bounded between a maximum and minimum value (T_(z,min) andT_(z,max)) to serve as constraint points in the simulation.

In an example, a relationship between the zone temperature (Tz) and theset point temperature (Tspt) can be quantified in the optimization. Forexample, the zone temperature (Tz) can be quantified based on an averagetemperature (T_(average)), a value of temperature in a setting vs.(T_(instantaneous)), or represented by a controller function. When achange is made to a temperature set point, the system may notnecessarily reach the value set. Therefore, the zone temperature (Tz)can differ from the set point temperature (Tspt). According to a system,method and apparatus herein, the differences between the zonetemperature (Tz) and the set point temperature (Tspt) can be quantifiedby computing a conversion standard between Tz and Tspt, or bydetermining T as function of a controller setting.

In an example, the dynamic simulation model of the at least one energyasset according to the principles described herein includes a total loadas a function of time. According to the principles herein, the forecasttotal load is computed as a combination of a load term dependent on anenvironmental condition (a weather dependent term) and a load term thatis independent of an environmental condition (a weather independentterm).

In an example implementation, the weather independent (WI) load forecastis modeled based on a time series, such as a schedule dependentfunction. The weather independent (WI) load can be modeled using aFourier series, a frequency dependent relationship. In a first instance,the weather independent load can include terms to represent the weekendsand holidays WI load equation, such as modeled by Eq. 28:

$\begin{matrix}{P_{{WI}_{weekends}} = {a_{0} + {\sum\limits_{h = 1}^{h}\left( {{a_{h}{\cos ({nx})}} + {b_{h}{\sin ({nx})}}} \right.}}} & {{Eq}.\mspace{14mu} 28}\end{matrix}$

where x=time and where h=number of hours/2-1. In a second instance, theweather independent load can also include terms to represent the weekdayWI load equation, such as modeled by Eq. 29:

$\begin{matrix}{P_{{WI}_{weekdays}} = {a_{0} + {\sum\limits_{h = 1}^{h}\left( {{a_{h}{\cos ({nx})}} + {b_{h}{\sin ({nx})}} + {\sum\limits_{d = 1}^{d}\left( {{c_{d}{\cos ({my})}} + {b_{d}{\sin ({my})}}} \right.}} \right.}}} & {{Eq}.\mspace{14mu} 29}\end{matrix}$

where x=time, y=day type, and h=number of hours/2-1.

In an example implementation, the weather dependent (Wd) load forecastis modeled based on a Montgomery equations (Eq. 30):

$\begin{matrix}{{P_{plant} = {a_{0} + {a_{1} \cdot N_{{chillers},{active}}} + {a_{2} \cdot {\sum\limits_{i - 1}^{{Nchillers},{active}}Q_{{chill},i}}} + {a_{3} \cdot \left( {\sum\limits_{i - 1}^{{Nchillers},{active}}Q_{{chill},i}} \right)^{2}} + {a_{4} \cdot {\sum\limits_{i - 1}^{{Nchillers},{active}}T_{{amb},{wb},i}}} + {a_{5} \cdot \left( {\sum\limits_{i - 1}^{{Nchillers},{active}}T_{{amb},{wb},i}} \right)^{2}} + {a_{6} \cdot {\sum\limits_{i - 1}^{{Nchillers},{active}}T_{{CHWS},i}}} + {a_{7} \cdot \left( {\sum\limits_{i - 1}^{{Nchillers},{active}}T_{{CHWS},i}} \right)^{2}} + {a_{8} \cdot {\sum\limits_{i - 1}^{{Nchillers},{active}}{Q_{{chill},i} \cdot {\sum\limits_{i - 1}^{{Nchillers},{active}}T_{{amb},{wb},i}}}}} + {a_{9} \cdot {\sum\limits_{i - 1}^{{Nchillers},{active}}{Q_{{chill},i} \cdot {\sum\limits_{i - 1}^{{Nchillers},{active}}T_{{CHWS},i}}}}} + {a_{10} \cdot {\sum\limits_{i - 1}^{{Nchillers},{active}}{T_{{amb},{wb},i} \cdot {\sum\limits_{i - 1}^{{Nchillers},{active}}T_{{CHWS},i}}}}}}}\mspace{79mu} {P_{{ahu},{pair}} = {a_{0} + {a_{1}{\overset{.}{v}}_{\sup,{pair}}} + {a_{2}{\overset{.}{v}}_{\sup,{pair}}^{2}}}}\mspace{79mu} {P_{{ahu},{total}} = {N_{{ahu},{pairs}} \cdot P_{{ahu},{pair}}}}} & {{Eq}.\mspace{14mu} 30}\end{matrix}$

The Montgomery equations can be trained based on historical datarepresentative of operation characteristics of energy assets, todetermine coefficients of model that model the energy assets. Asnon-limiting examples, the data can be historical electric meter datarepresentative of the operation of controllable energy assets such asthe chillers or air-handlers electric meter. For example, data can becollected as the chiller cools water and/or the air handler uses thecooled water from the chillers to cool the building asset. If such datais not available and/or costly, data loggers can be used to recordquality data at chillers and air-handler.

According to an example of apparatus, systems and methods describedherein, the term in Qchill in the Montgomery equations is replaced by aterm in “u/k”, where u is the bias temperature setpoint shift, in unitsof ° F., and k is a constant determined by regression (see Eq. 31).

$\begin{matrix}{P_{{chiller}_{i}} = {a_{0} + {a_{1} \times N_{chillers}} + {a_{2}{\sum\limits_{n = 1}^{N_{chillers}}\left( \frac{u_{i}}{k_{i}} \right)_{n}}} + {a_{3}{\sum\limits_{n = 1}^{N_{chillers}}\left( \frac{u_{i}}{k_{i}} \right)_{n}^{2}}} + {a_{4}{\sum\limits_{i = n}^{N_{chillers}}{W\; B\; T_{i_{n}}}}} + {a_{5}{\sum\limits_{n = 1}^{N_{chillers}}\left( {W\; B\; T} \right)_{n}^{2}}} + {a_{6}{\sum\limits_{n = 1}^{N_{chillers}}{\left( \frac{u_{i}}{k_{i}} \right)_{n}{\sum\limits_{n = 1}^{N_{chillers}}{W\; B\; T_{i_{n}}}}}}}}} & {{Eq}.\mspace{14mu} 31}\end{matrix}$

Where WBTi=ambient wet-bulb temperature or ambient enthalpy, ui is thecontrol point (bias shift in ° F.). and k_(i) is a constant derived fromregression analysis. For example, the constant “k” in the term “u/k” canbe determined based on a regression using the training (historical)data.

In an example implementation, the term in T_(amb,wb) can be replaced bya term in enthalpy in the P_(plant) equation, where applicable.According to another example implementation, the temperature bias can beto the term in the P_(ahu,total) equations.

$\begin{matrix}{P_{{AHU}_{i}} = {a_{0} + {a_{1} \times \frac{u_{i}}{k_{i}}} + {a_{2} \times \left( \frac{u_{i}}{k_{i}} \right)^{2}} + {a_{3} \times Q_{c\; 1{gHVAC}_{i}}} + {a_{4} \times Q_{c\; 1{gHVAC}_{i}}^{2}} + {a_{5} \times \frac{u_{i}}{k_{i}} \times Q_{c\; 1{gHVAC}_{i}}}}} & {{Eq}.\mspace{14mu} 32} \\{\mspace{79mu} {P_{{WeatherDependent}_{i}} = {P_{{chillers}_{i}} + {\sum\limits_{n = 1}^{N_{AHUs}}P_{{AHU}_{i}}}}}} & {{Eq}.\mspace{14mu} 33}\end{matrix}$

In Eqs. 31 and 32, Q_(clg,HVAC) represents the forecast cooling loadrequired from solution of the zone temperature state-space equation. Inan example implementation, the term in Q_(clg,HVAC) can be replaced bythe zone temperature (Tz.).

FIG. 13 shows a non-limiting example flowcharts of example processes forperforming the dynamic simulation modeling, including training andvalidating the model. At 30, the total historical data available aboutthe total load for training the model is evaluated.

As also shown in FIG. 13, the dynamic model terms in the load model canbe determined, trained and validated. At 31, it is determined whethersufficient historical data about load (MW) is available. If the data isinsufficient, the process may not continue (60). If there is sufficientdata, the data is separated (33) into the contribution for the chillersversus the contribution from other systems, including the air-handlers(AHU). If the data cannot be separated into chiller versus air-handlerloads, meter readings can be taken (34) to gather data. Aninitialization occurs at 35 to determine the coefficients of P_(chiller)and P_(AHU). At learning block 36, controlled tests are carried out toderive the coefficients of u/k. The results of provide the dynamic modelterms in the load.

As also shown in FIG. 13, the dynamic model terms in the temperaturemodel are also determined, trained and validated. At 41, it isdetermined whether sufficient historical data about Tz is available. Ifthe data is insufficient, the process may not continue (60). At 43, itis determined whether sufficient historical data about Qclg,HVAC isavailable. If no, meter readings can be taken (44) to gather data. Aninitialization occurs at 45 to run an algorithm to determine the R's andthe C's of the thermodynamic properties model (such as but not limitedto a genetic algorithm (GA)). At 45, a regression is also run todetermine the Cz. At learning block 46, controlled tests are carried outto determine the global optima over the system for the R's, C's and Cz.

According to the systems, apparatus and methods described herein, theprocedure for determining the dynamic simulation load model (blocks31-36 of FIG. 13) can be performed independently of the procedure fordetermining the dynamic simulation temperature model (blocks 41-46 ofFIG. 13).

According to the example flowcharts of FIG. 13, the results of thedynamic simulation load model (blocks 31-36) can be performedindependently of the procedure for determining the dynamic simulationtemperature model (blocks 41-46). Furthermore, the procedures of blocks31-36 may be performed prior to, concurrently with, or subsequent to theprocedures of blocks 41-46.

In the example of FIG. 13, an end-to-end test can be performed at 54 tovalidate the forecasting robustness of the dynamic simulation models.Once successfully validated (58), the models are ready for use in any ofthe systems, methods, and apparatus described herein to perform anoptimization as part of an objective function.

Day-Ahead Engineering-based Models

In a non-limiting example, a day-ahead engineering-based model can begenerated using a system, method or apparatus described herein. Anexample objective function can be generated to facilitate adetermination of the suggested operating schedule for at least oneenergy asset that includes a controllable energy asset, based at leastin part on a physical model of the thermodynamic property of the atleast one energy asset and at least in part on data representative ofmodel parameters. The model parameters can be one or more of modelparameters are: (a) an operation characteristic of the at least onecontrollable energy asset, and (b) a projected environmental conditionduring time period T.

An example suggested operating schedule can be generated based on anoptimization of the objective function over time period T. The objectivefunction is determined based on a dynamic simulation model of the energyprofile of the at least one energy asset, a customer baseline (CBL)energy profile for the at least one energy asset (see, e.g., sectionabove on the terms of the engineering-based dynamic simulation model),and a forecast wholesale electricity price, over time period T,associated with participation of the at least one energy asset in amarket. As non-limiting examples, the market can be a wholesaleelectricity market and/or a regulation market. For example, the forecastmarket price can be a forecast wholesale electricity price associatedwith participation of the at least one energy asset in a wholesaleelectricity market. As another example, the forecast market price can bea forecast regulation price associated with participation of the atleast one energy asset in a regulation market. The dynamic simulationmodel of the day-ahead EBM can be made adaptive to physical changes inthe at least one energy asset based at least in part on the physicalmodel of the thermodynamic property of the at least one energy asset.The dynamic simulation model can be trained using the datarepresentative of the model parameters prior to time period T. Theoperation of the at least one energy asset according to the suggestedoperating schedule, over a time period T, facilitates generation ofenergy-related revenue based at least in part on the market, such as butnot limited to the wholesale electricity market and/or the regulationmarket.

The day-ahead engineering-based model can be configured as a day-aheadpredictive engineering-based building model (OD^(t−1)) model. TheOD^(t−1) model is a representation of the building and systems to beutilized for the day-ahead forecasting and optimization, and can be madeadaptive to any changes in the building asset. The OD^(t−1) model can bebased on a semi-non-linear regression. The OD^(t−1) model can be used togenerate operating schedules in advance of a time period over which theenergy asset is operated. As a non-limiting example, the OD^(t−1) modelcan be used to generate an operating schedule a day in advance (e.g.,over 48 time steps in 24 hour time period). For example, the OD^(t−1)model can be used to generate models for projected/expected load, zonetemperature and/or return air pressure for the energy assets. Theprojected/expected load, zone temperature and/or return air pressure areintroduced into an objective cost function. An optimization tool of thesystem can be used to generate a suggested operating schedule for theenergy asset based on an optimization, over time period T, of theobjective cost function. The suggested operating schedule can beimplemented for operation of the building asset during time period T. Anexample of an OD^(t−1) model can be derived based on the physical modeldescribed in the section above, for modeling the zone temperature andthe load as a function of time.

A customer baseline model (CBL) may be utilized for day-ahead andreal-time prediction and optimization. The CBL can be generated based onthe data representative of model parameters of the energy asset prior totime period T. For example, the model can be trained using an amount ofpast data of the energy assets during operation, such as but not limitedto at least about 15 days, about 30 days, about 45 days, or more, ofpast data. The training data allows for the determination of thecoefficients of the model parameters described above in connection withFIGS. 9-13 for a given system including the at least one energy asset(including a building asset).

The past data can be data representative of operation characteristic ofthe at least one controllable energy asset. For example, the operationcharacteristics can be a load use schedule, or a setpoint. Asnon-limiting examples, the data representative of thermodynamic propertyof the at least one energy asset can be past data, such as but notlimited to at least about 15 days, about 30 days, about 45 days, ormore, of past data, about thermodynamic measures.

Non-limiting examples of data representative of thermodynamic propertiesinclude of at least one of an occupancy schedule of the building asset,a relative humidity of the building asset, a temperature of the buildingasset, a lighting level of the building asset, a chilled watertemperature, an air temperatures, and an ice loop temperature.

Non-limiting examples of the data representative of the projectedenvironmental condition during time period T include at least one of anambient temperature of the environment in which the building asset islocated; a humidity of the environment in which the building asset islocated; an amount of solar irradiance of the environment in which thebuilding asset is located, an amount of cloud cover of the environmentin which the building asset is located, an outside air temperature, anoutside air humidity, an outside air enthalpy, an outside air wet bulbtemperature, a dewpoint temperature, and a heat index.

In a non-limiting example, the dynamic simulation model can be trainedusing the data representative of the operation characteristic of the atleast one controllable energy asset and the thermodynamic property ofthe at least one energy asset, during operation of the at least oneenergy asset under similar environmental conditions to the projectedenvironmental condition during time period T.

In an example, a suggested operating schedule may be provided as anumber of bias signals that are stored to a memory and/or transmitted tothe EMS of an energy customer. The bias signals can be implementedaccording to the suggested operating schedule to control the operationof the energy assets to facilitate the generation of the energy-relatedrevenue, as described hereinabove.

In another example implementation, managing various energy assets may beimplemented according to the suggested operating schedule in a phasedmanner to facilitate revenue generation from wholesale electricitymarkets, regulation markets, or other ancillary markets. Such asuggested operating schedule may be provided as an interruptible loadfunction, or as a modulation signal. For an interruptible load function,the instructions would specify the types of components, equipment,and/or computing resources to power off or power on during various timeintervals of time period T, and in what combinations according to thesuggested operating schedule. For a modulation signal, the instructionswould specify the types of components, equipment, and/or computingresources to whose operation parameters can be increased or decreased,in order to increase or decrease its load during various time intervalsof time period T, and in what combinations according to the suggestedoperating schedule. Each phase of the suggested operating schedule maytarget a specific energy asset resource, and phases may be implementedsequentially over time for a given set of energy assets, or in anycombination concurrently for a given set of energy assets. For example,a first phase may target the HVAC system of a building; a second phasemay target backup generators, other distributed energy generationresources (e.g., renewables), and energy storage resources (e.g.,batteries) (collectively “power resources”); and a third phase maytarget other specific resources of a site, such as but not limited tothe different type of equipment in a building complex of manufacturingsite, or the server computing load of the multiple servers at a buildingthat houses a data center.

Real-Time Engineering-based Models

In a non-limiting example, a real-time engineering-based model can begenerated using a system, method or apparatus described herein. TheOD^(t) model can be based on a linear representation of the building andsystems to be used in live operation for adaptive optimization andshort-term forecasting. In an example, the OD^(t) model can be used togenerate models for projected/expected load, zone temperature and/orreturn air pressure that can be updated using a feedback mechanism, suchas but not limited to Kalman filtering, based on measurements duringoperation of systems of the energy assets, such as the building asset.

The OD^(t) model can be updated in real time (adjustable) using a KalmanFilter (KF) algorithm. For example, the OD^(t) model can be updatedusing a KF algorithm at regular intervals (such as but not limited toabout every minute, or about every few minutes, or about every halfhour). The KF is a recursive tool that obtains optimal estimations ofthe system state as well as the online identification of parameters ofthe system state-space equation. The KF can be determined when the modelis run for state estimation, and/or for online parameter identificationbased on targeted model accuracy. The OD^(t) model can be run andupdated using KF as many times as required to reach a solution. TheOD^(t) model can be described as a linear state-space system.

In a non-limiting example, the KF can be implemented as described inFIG. 14. The state estimate and/or the model parameters can be updatedin real-time, such as but not limited to about each minute, or every fewminutes.

x _(k) =G _(k) *x _(k−1) +I _(k) *u _(k) +e _(k)

z _(k) =H _(k) *x _(k)+η_(j)  Eq. 34

Nomenclature of abbreviations in FIG. 14 is as follows:

-   -   x_(k) is the state vector at time step k    -   G_(k) is the state transition matrix at time step k    -   u_(k) is the input at time step k    -   I_(k) is the input matrix at time step k    -   e_(k) is the process noise, following normal distribution i.i.d        N(0,Q_(k))    -   z_(k) is the observation vector at time step k    -   H_(k) is the observation matrix at time step k    -   η_(k) is the observation noise, following normal distribution        i.i.d N(0,R_(k))

As shown in FIG. 14, the KF can be implemented, during time period T ofoperation of the energy asset(s) according to the suggested operatingschedule, as a time update in a first stage and as a measurement updatein a second stage. In the first stage, as described in FIG. 14, thesystem state is predicted at a time t during time period T based on thesuggested operating schedule. The estimation error is propagated toprovide the time update. In the second stage, a measurement update isperformed. The predicted state is compared to the measured state. As anon-limiting example, a measurement is made of at least one of (i) anoperation characteristic of a controllable energy asset, (ii) athermodynamic property of the energy asset, (iii) an actualenvironmental condition. Based on the comparison of the predicted stateand the measured state, the estimation error is updated. The parametersof the suggested operating schedule are updated based on the results ofthe KF process to provide an updated operating schedule. Using the KF,the state estimate and/or the model parameters can be updated inreal-time, such as but not limited to about each minute, or every fewminutes during time period T.

Day-After Engineering-based Models

In a non-limiting example, a day-after engineering-based model can begenerated using a system, method or apparatus described herein. In anon-limiting example, the configuration of the day-afterengineering-based model can be used to generate the CBL for the system.

The OD^(t+1) model can be based on a highly non-linear representation ofthe building and systems. Three OD^(t+1) models can be developed toclosely match the customer load under business-as-usual operations aswell as under the control of the operating schedule of the real-timeOD^(t) model. All of the CBL models can be run and the model with thebest accuracy can be selected for settlement purposes with an industryparticipant in a market and/or other RTOs/ISOs. The OD^(t+1) models canbe expressed as:

Non-Linear Kernel CBL Model

The non-linear kernel can be used to generate a non-linearrepresentation of a system that includes the at least one energy asset.For example, for an energy asset that is a building asset, the kernelCBL model may take the form of either Eq. 35, Eq. 36 or Eq. 37:

ln(Load(t))=A _(i)×Weatherinput_(i)(t−j)+B _(i)×ZoneTemperature(t−f)+C_(i)×ln(PriorLoad(t−j))+D _(i) ×HVACSchedule(t−j)+F_(i)×ControlSetpoint(t−j)+G _(i) ×HVACTemperature_(i(t−j))

Load^(t)(t)=A _(i)×WeatherInput_(i)(t−j)+B _(i)×ZoneTemperature(t−f)+C_(i)×PriorLoad²(t−f)+D _(i) ×HVACSchedule(t−j)+F_(i)×ControlSetpoint(t−j)+G _(i) ×HVACTemperature_(i)(t−j)  Eq. 36

$\begin{matrix}{{\left( {{Load}(t)} \right){neuralnetwork}} = {{funtion}\left\lbrack \left( {{{PriorLoad}\left( {t - i} \right)},{{WeatherInput}\left( {t - 1} \right)},{{PriorZoneTemperature}\left( {t - i} \right)},{{weekday}\left( {t - i} \right)},{{HVACSchedule}\left( {t - i} \right)}} \right. \right.}} & {{Eq}.\mspace{14mu} 37}\end{matrix}$

FIG. 15 depicts the neural network of Eq. 37, and shows the functionalform of applicable transfer functions, including a log-sigmond transferfunction and a tan-sigmond transfer function.

FIG. 16 depicts the parallel neural network architecture that can beused where it re-iterates using the output from the previous iteration.The series-parallel architecture can be used using the input of a statefunction and some data representing prior measurements of load.

Higher order representation may be used for load and/or all inputs. TheCBL model may be used for real-time optimization and prediction. In suchcases, the KF may be a non-linear tool such as an extended Kalman Filteror similar.

Artificial Neural Network AutoRegressive External Input (ANN-NARX) CBLModel

A feed forward NARX network architecture of the form shown in Eq. 38 canbe used in an example ANN-NARX for generating a CBL Model:

Load(t)=(7)

function[(Load(t−1),Load(t−2), . . . , Load(t−

j),WeatherInputs(t), . . . , WeatherInputs(t−j),ZoneTemperatures(t−

j),Weekday(t−j),HVACSchedule(t−j),ControlSetpoints(t−

j),HVACTemperatures(t−j)]  Eq. 38

In a non-limiting example, the neural network has two hidden layers,each with ten neurons.

Another example engineering-based model that is applicable to thesystems, methods and apparatus described herein is shown in Eq. 39.

$\begin{matrix}{{{kW} = {{kWi} + {kWd}}}{\frac{{Tz}}{t} = {{A^{\prime}*O\; A\; T} + {B^{\prime}*T_{z}} + {C^{\prime}*\left( {T_{spt} + {bias}} \right)}}}{{kWd} = {{occ}*\left\lbrack {{A*{f({weather})}} + {B*{f({indoor})}}} \right\rbrack}}{{kWi} = {{f\left( {{time},{day}} \right)} = {{time}\mspace{14mu} {series}\mspace{14mu} {model}}}}} & {{Eq}.\mspace{14mu} 39}\end{matrix}$

Eq. 39 show an example multiple regression engineering-based model towhich the physical model of the thermodynamic property can be added. Themodel includes a total load term (in units of kiloWatt (kW)) as acombination of a load term that is independent of environmentalconditions (a weather independent load term kWi) and a load term that isdependent on environmental conditions (the weather dependent load termkWd). The weather dependent load term kWd is modeled as a term in anoutside environmental condition (such as but not limited to the outsideair temperature (OAT) and the outside air humidity (RH)) and the indoorinputs (such as but not limited to the zone temperature and the zonehumidity), weighted by a value of “occ” (the building occupancy scheduleor by the HVAC operating schedule, which reflects when the building isoccupied vs. unoccupied). The kWd term may depend mainly on the HVAC. Astatistical analysis or other anlysis tool can be implemented todetermine model parameters in the kWd model. The weather independentload term kWd is modeled as a time series that can vary with the day ofthe week and/or with the time of day. For example, the weatherindependent load term on a weekday can differ from that on a weekend,and can also vary according to time of day for a given day. For example,the weather independent load term can differ between business hours andoff-peak times. In addition, the weather independent term may vary witha time series dependent on season, weekdays, holidays/weekend. A Fourieranalysis can be used to solve the weather independent load term.

In a non-limiting example, load modeling results with application of themultiple regression are as follows. The coefficient of variation fromactual load is between about 3% to about 10% within a day type; andwithin about 10-20% overall. The example building asset load was onaverage 700 kW; the multiple regression model root-mean-square error(RMSE) varied between 49 kW to 65 kW per day type.

The model of Eq. 39 also includes a term for modeling the zonetemperature (Tz) of a building energy asset based on a regression. Thetime-dependent variation in the zone temperature (Tz) is modeled as aregression over the outside air temperature (OAT), the zone temperature(T_(z)), the target zone (setpoint) temperature (T_(spt)) and atemperature deviation (as a bias). As described above, a term thatmodels the thermodynamic properties of the system is added, whichincreases the accuracy of the modeling.

The coefficients A′, B′, C′, are determined during the model trainingand fitting process. These coefficients can be updated in real-timethrough a feedback and auto-corrective loop, e.g., during a real-timeengineering-based operation.

The comfort index (CI) is used in this example to provide a measure ofan occupant's comfort on a scale of 0 to 100, where CI=100% when Tz iswithin the cooling and heating setpoints (72 F to 78 F) and RHz iswithin 40%-60% (consistent with the American Society of Heating,Refrigerating and Air-Conditioning Engineers (ASHRAE) Standard 55-2007).

FIG. 17 is a plot showing the relationship between different values ofthe comfort index (CI), the outside air humidity and the humidity.

Systems For Implementation of an engineering-based Model

FIGS. 18-22 show non-limiting examples of functional block diagrams ofsystems and apparatus that can be implemented to perform any of theengineering-based models according to the principles described herein.FIG. 18 shows a functional block diagram of a PID controller. FIGS.19-22 show functional block diagrams of configurations of systems andapparatus for implementing a CBL computation, a day-ahead EBM modeling,a real-time EBM modeling, a day-after EBM modeling, respectively.

Non-limiting examples of data that can be received by a communicationinterface of the example systems and apparatus as input date toimplement an engineering-based model include: a) a customer's totalload, b) a customer's HVAC load (if applicable), c) customer's weightedaverage building temperature, and d) a customer's local weatherconditions.

In an example, a test may be used to validate the customer's load modelsand control.

Building Parameters Database for Faster Implementation

According to the principles described herein, the parameters of a EBMmodel (including the day-ahead, real-time, day-after and CBL models),may be stored in a database to result in faster implementation. Forexample, once a EBM model is built and trained as described herein, itmay be implemented in an optimization over an objective function togenerate suggested operating schedules on other physically comparableenergy assets (including on physically similar building assets). Thedatabase may be used to classify buildings and parameters in categories,such as but not limited to the following categories:

a) Building Envelope: light, medium or heavy construction

b) Building Fenestration: light or heavy fenestration

c) Building Type: commercial, industrial, residential or institutional

Proportional Integral Derivative (PID) Loop

A PID controller algorithm also is developed to correct in real-time thebias signal sent to the building and ensure the target load reduction isachieved. The PID can be used to provide a measure of error (includingranges of values within the error) around a bias signal and to refine abusiness-as-usual approximation. BMS refers to the building managementsystem that controls, e.g., equipment in the building (control signalscan be sent to the BMS).

An example PID operates as illustrated shown in the functional blockdiagram of FIG. 18. Non-limiting examples of a “control” is a load(represented by kW), a zone temperature (represented by Tz), or othercontrollable asset (any other parameter sought to be controlled,including but not limited to a voltage). The PID model of FIG. 18 may beimplemented so that the “control” is only the load (kW), only the zonetemperature (Tz), only one of the other controllable assets, or anycombination of two or more of the load, the zone temperature, and one ormore of the other controllable assets. Applying the PID to load (kW) canbe used to achieve an economic priority, i.e., ensuring that a load (kW)target is maintained within a predetermined allowable economic range.Applying the PID model to zone temperature (Tz) can be used to achieve acomfort priority, i.e., seeking to ensure that a zone temperature (Tz)target is maintained within a predetermined allowable comfort zone. Inan implementation, the PID model is applied to both the load (kW) andthe zone temperature (Tz) as “controls”.

System Configurations For Implementing Engineering-based Models

Non-limiting example system configurations for implementing the exampleengineering-based models are described in connection with FIGS. 19-22.

In each figure, the term M&V refers to measurement and verification; ADPrefers to a dynamic programming solver. The terms RH and RH_(z) refer tothe relative humidity of outside air (RH) and building humidity(RH_(z)). T and T_(z) refer to temperature, where Tz is a target zonetemperature.

Engineering-based Models and Optimizers

Example outputs from the engineering-based models are predicted load andpredicted zone return air temperature. Examples of optimizations thatcan be run include according to the systems, apparatus, and methodsdescribed herein include a day-ahead optimization and a real-timeoptimization.

Day-Ahead (OD^(t−1)) Optimization

FIG. !9 shows a functional block diagram of a system configuration forapplication of an optimization during the day-ahead EBM computation. TheOD^(t−1) predictive model can be utilized to project the load in theenergy asset (s), such as but not limited to a building. Based on priceand weather forecasts, an optimal operating schedule of setpoints orbiases can be determined to maximize revenues while maintaining comfort.The day-ahead optimization can be constrained by the zone temperatureequation (2). Boundaries can be set for setpoint control range oftypical occupants' comfort as determined by an energy customer or as setby the ASHRAE Standard 55-2010.

For poorly sensed buildings, including buildings with insufficienttraining data to capture the effect of temperature setpoints on the realzone temperature, a hybrid zone temperature equation of following formcan be utilized:

A*RAT(t+1)=B*(OAT−RAT)(t)C*SPT(t)−D*RAT(t)+E  Eq. 40

If the hybrid RAT equation does not provide robust constraint foroptimization, change in temperature setpoint can be limited to themaximum recorded change in actual zone temperature.

Real-Time (OD^(t)) Optimization

FIG. 19 shows a functional block diagram of a system configuration forapplication of an optimization during the real-time EBM computation. Inreal-time operation, the OD^(t) model can be used to predict the load ofthe next time step. The optimizer can be run after each Kalman Filterupdate.

Non-Limiting Example Implementation

The engineering-based model can be applied to all building types,including but not limited to poorly-sensed buildings. Results ofnon-limiting example implementations are described. In these examples,the term AHU relates to the status of mechanical equipment (such as aHVAC system), OAH refers to outside air humidity, SPT refers tosetpoint, and Qirr refers to solar irradiance.

EBM Models Applied To First Example Building Asset

A non-limiting example is described of application of EBM models formodeling energy assets of a First Example Building. The EBM models canbe used, according to the systems, methods and apparatus describedherein, to facilitate generation of energy-related revenue based onoptimization of an objective cost function.

Day-Ahead EBM

Based on data representative of operation characteristics and thethermodynamic properties of the First Example Building Asset, aday-ahead EBM model of the building asset is computed as describedherein.

Optimizer Module

In an example, an optimizer can be implemented to provide at least onesolution set with respect to minimizing energy costs of a customer site(including the at least one energy asset). As described hereinabove, theoptimizer can be implemented to minimize the net energy related costs(expressed as follows) over a time period T to determine the possibleenergy-related revenue that may be derived:

$\begin{matrix}{{Min}\left\lbrack {\sum\limits_{t}^{T}\left( {{{Supply}\; {\$ (t)}} - {{DR}\; {\$ (t)}}} \right)} \right\rbrack} & {{Eq}.\mspace{14mu} 41}\end{matrix}$

where the term Supply$(t) indicates the total cost to supply the energyand demand response revenue. As a non-limiting example, the optimizercan be implemented according to the following equations.

Minimize[T$_(dev)+Emission$+Gen$+Supply$−DR$]  Eq. 42

where

-   -   T$_(dev)=Cost of deviations from the business-as-usual comfort        levels (including BAU temperature level)    -   Emission$=Cost/value of deviations from the emission target    -   Gen$=Cost of electric power production by the customer energy        generating assets    -   Supply$=Cost of electric power supply from the Load Serving        Entity or the Electric Distribution Company

DR$=max(0,(CBL−Load))*(LMP−G&TRate)  Eq. 43

-   -   where        -   CBL=Customer baseline        -   Load=Actual customer usage        -   LMP=Locational Marginal Prices        -   G&T Rate=Generation & Transmission Rate

In an example implementation, the minimization (Minimize|_(T)) can becomputed, for a specified time period T, based on Eq. 41 or 42 for thetime varying parameters. As a non-limiting example, time period T can bea period of 24 hours, 48 hours, or any other specified time period. Inthis example, an optimizer can be implemented to perform a methodaccording to minimization (Minimize|_(T)), for the specified time periodT. In an example implementation, the minimization can be computed as acontinuous function over the entire time period T. In another exampleimplementation, the minimization can be computed over discrete portionsof the function for several time intervals (Δt) that sum to time periodT. As a non-limiting example, the minimization can be computed over 48different half-hour time intervals, for a total time period of T=24hours.

An aspect of an optimizer is that the optimization determines asuggested operating schedule that can facilitate an overall economicbenefit (including deriving energy-related revenue) over the entire timeperiod T, even if the operating schedule may call for a mode ofoperation during a given time interval (t₁ to t₂) during time period Tthat may not take advantage of the lowest energy market price or lowestcost of energy production during that time interval (t₁ to t₂). Forexample, the suggested operating schedule may determine an operatingschedule that calls for increased load if, for example, the load is usedto charge an energy storage asset that can be committed to a regulationmarket if the regulation market price is sufficiently favorable to theoverall economic benefit, for the entire time period T, when thesuggested operating schedule is performed over the entire time period T.

To generate the suggested operating schedule, the optimizer can performcomputations over various parameters, such as but not limited to theforecast wholesale market price over time period T for participating inthe energy market, the regulation market price available to the customerand over what time interval, other potential energy-related revenue(including synchronized reserve markets and capacity markets), and theenergy responsive behavior of each of the customer's energy assets. Forexample, the suggested operating schedule may includeprocessor-executable instructions that cause a backup generator to becommitted to an economic demand response market, a synchronized reservemarket, or a capacity market as a part of the overall optimization overthe time period T. In another example, the building of the customersite, the occupancy of the building, the HVAC systems; chillers;ventilators; heaters; lighting; or other similar energy assets canexhibit thermal inertia, i.e., it can take longer time scales for suchenergy assets to respond to a change in signal to reach a change ofstate (e.g., get to a lower temperature). The operating schedule caninclude processor-executable instructions to commit these types ofenergy assets to respond to an economic demand response market, acapacity market and/or a power quality market at some time intervalduring time period T based on the modulation of use of these energyassets. In other examples, the state of charge of a battery at a givenpoint in the day determines whether it has sufficient capacity to becommitted to any of the economic demand response market, regulationmarket, synchronized reserve market, capacity market or power qualitymarket. As another example, the suggested operating schedule can includeprocessor-executable instructions that cause energy assets (includingthe servers in a computing facility) to be committed to a regulationmarket based on modulating the operation of the controllable energyasset, where the controllable energy asset can respond on a fasttimescale (e.g., according to regulation signals that can changepotentially at 2-second time intervals). Non-limiting examples of themodulation of the operation of controllable energy assets includechanging the speed of fans, changing the computing levels of servers andother computing equipment, modifying variable frequency and/or variablespeed of components (such as but not limited to modifying the frequencyand/or speed of a motor, a fan, any other ventilation equipment, and/ora pump), cooling rate/speed of the HVAC and a chill rate of the chiller.Given that increased load consumption of a controllable energy asset cancause them to heat, and as a result may also require increased cooling,there can be an interaction term between the modulation of controllableenergy assets and the usage of energy assets such as HVAC systems;chillers; ventilators; or heaters. As a result, implementation ofcontrollable energy asset in, e.g., the regulation market, can affecthow energy assets such as HVAC systems; chillers; ventilators; orheaters are implemented in the energy markets, through time-basedinteraction terms. The systems, methods and apparatus described hereinperform optimization that evaluate the types of energy assets in eachcustomer site, the state of each of the various energy assets at eachcustomer site, the markets available to each different customer site andthe forecast prices for each such market, and the behavior of eachenergy asset, all over a specified time period T. The result isgeneration of a suggested operating schedule for the various energyassets (and/or for the controller of each energy asset), that, whenexecuted, can generate energy-related revenue, over a time period T,associated with operation of the energy assets according to theoperating schedule.

Energy Management System

FIG. 23 illustrates a block diagram of an energy management environment100 that includes an energy management system 110 to facilitategeneration of revenue from energy markets, according to the principlesdescribed herein. In FIG. 23, energy customers are the end-users and/orconsumers of electricity supplied by a retail electricity supplier (viaelectricity grid 105). Additionally, some customers may have electricitygeneration capabilities for providing electricity back to the gridand/or for supplying electricity to the customers' ownelectricity-consuming assets.

Energy management system 110 may include one or more processors 112, oneor more memory devices 114, and one or more communication interfaces115. Processors 112 may be configured to execute software applications(e.g., processor-executable instructions stored in one or more memorydevices 114), and/or work in tandem with other hardware components, formanaging the overall operations of energy management system 110. In aservice-oriented architecture (SOA), processors 112 may implement avariety of functionality for supporting the SOA. In one example,communication interfaces 115 may include a web interface and anenterprise service bus. Additional details regarding processors 112,memory 114, and communication interfaces 115 are described withreference to FIG. 24.

One or more system operators 116 may be associated with energymanagement system 110. System operators 116 may access energy managementsystem 110 via an operator portal 118. Operator portal 118 is the userinterface by which a system operator 116 may manage the process ofcreating an energy assets operating schedule. The optimized operatingschedule covers a chosen period of time. Once an optimized operatingschedule is transmitted to and accepted by the customer, in someexamples operator portal 118 may be used by a system operator 116 formonitoring and/or controlling a customer's energy assets in real time.

Customer sites 120 and end-users (energy customers) 128 of energymanagement environment 100 represent the customers and/or consumers ofthe electricity supplied by retail electricity suppliers via the grid105. Customer sites 120 may be any electricity-consuming and/orelectricity-generating environments, such as, but not limited to, abuilding or group of buildings belonging to energy grid customers.End-users 128 may be any individuals associated with customer sites 120.Examples of end-users 128 may include building supervisors, companyemployees, company executives, and the like. Each customer site 120 mayinclude one or more energy assets 126. Energy assets 126 may be anyconfiguration of one or more energy usage assets, one or more energystorage assets, one or more energy generation assets, one or morerenewable energy assets, and any combinations thereof. Groups of energyassets 126 and/or buildings associated with a certain customer site 120may be in close physical proximity to each other or may be physicallydistant and even separated by time zones.

A monitoring and control system 122 may be installed at each customersite 120. One example of monitoring and control system 122 is a buildingmanagement system (BMS). Another example of monitoring and controlsystem 122 is a building automation system (BAS). The main function of aBMS and BAS is to manage the environment within a building or group ofbuildings. For example, the BMS or BAS may control temperature, carbondioxide levels, and humidity within a building via certain energy assets126. In one example, an end-user 128 of a certain customer site 120 usesmonitoring and control system 122 to monitor and/or manage the energyassets 126 thereof. Monitoring and control system 122 may be anycommercially available BMS or BAS, such as those supplied by JohnsonControls, Inc (Milwaukee, Wis.), Automated Logic Corporation (Kennesaw,Ga.), and Honeywell International, Inc (Morristown, N.J.).

The web-based operator portal 118 and/or the web-based customer portal130 may be accessed via any web-enabled device, such as, but not limitedto, any desktop computer, laptop computer, tablet computer, net-bookcomputing device, handheld computing device, personal digital assistant,enterprise digital assistant, portable digital assistant, cell phone,smart phone, and the like.

Additionally, energy management environment 100 may include any otherentities that may be useful to (and communicate with from time to time,e.g., via network 170) energy management system 110 for operating,using, and/or controlling energy assets 126 of customer sites 120.Examples of other entities may include, but are not limited to, energyregulatory agencies 150 (e.g., FERC) and ISOs/RTOs 152. The energyregulatory agencies 150 and/or ISOs/RTOs 152 may be a source of anyinformation that is useful to energy management system 110 foroperating, using, and/or controlling energy assets 126 of customer sites120. Examples of useful information sources may include, but are notlimited to, rules information 154, and/or energy price information 156.Rules information 154 may be any rules, regulations, and/or guidelinesaccording to any authorized entity related to the electric powerindustry, such as, but not limited to, energy regulatory agencies 150and/or ISOs/RTOs 152.

Additionally, energy management environment 100 may include a source ofweather information 160. In one example, weather information 160 may beinformation supplied by a third party service, such as, but not limitedto, AWIS Weather Services, Inc. (Auburn, Ala.). In another example,weather information 160 may be information supplied by a national and/orregional weather service that may be accessed using the Internet via anetwork 170. Examples of weather websites may include, but are notlimited to, the NOAA National Weather Service website (at nws.noaa.gov),the Weather Channel website (at weather.com), and the WeatherUnderground website (at wunderground.com).

Weather information 160 may be useful to the optimization function ofenergy management system 110 with respect to predicting the actualenvironmental conditions in a building or group of buildings. Forexample, the optimization function may factor in the delta between theoutside temperature and inside temperature. Additionally, theoptimization function may factor in the amount of cloudiness withrespect to solar gain calculations. For example, the solar gain may belowest on a cloudy day, highest on a day that is not cloudy, andanything in between.

Additionally, energy management environment 100 may include certainemissions regulatory agencies 162 that may be the source of certainemissions information 164. Emissions regulatory agencies 162 may be anyfederal, regional, state, and/or municipal regulatory bodies thatfacilitate emissions trading programs. In the United States, theEnvironmental Protection Agency (EPA) is an example of an emissionsregulatory agency 162. In Europe, the European Union (EU) is an exampleof an emissions regulatory agency 162. Emissions information 164 mayinclude emissions cap information, cost information for buying emissionscredits, and/or price information for selling emissions credits.Additionally, emissions information 164 may include any publishedinformation about the local energy grid (e.g., energy grid 140) withrespect to the emission of pollutants and/or greenhouse gases (GHG).

Network 170 may be, for example, any local area network (LAN) and/orwide area network (WAN). Network 170 provides the communication linkbetween any and/or all entities of energy management environment 100.For example, energy management system 110, operator portal 118, customersites 120, customer portal 130, regulatory agencies 150, ISOs/RTOs 152and/or emissions regulatory agencies 162 may be connected via network170. Entities may connect to network 170 using any wired and/or wirelessnetworking protocols. Additionally, rules information 154, energy priceinformation 156, weather information 160, and/or emissions information164 may be accessed via network 170.

Once a model is created, the environmental conditions within thebuilding or group of buildings of a certain customer site 120 may besimulated based on the input criteria of the model. The simulationresults are then fed into an optimization function, which is a costminimization function that includes a combination of multiplesub-functions, of energy management system 110 that processes theinformation and generates an optimized operating schedule, within a setof constraints. That is, the optimization function of energy managementsystem 110 is used to create an operating schedule (for a chosen periodof time) for energy assets 126 of a customer site 120, wherein theoperating schedule for the energy assets 126 is optimized for reducingenergy costs, reducing emissions costs, and/or generating revenue fromenergy markets.

Energy management system 110 may also include a markets component (seeFIGS. 2 and 7) for interacting with any entities in the energy markets.In one example, the markets component processes settlements in anyenergy markets between the ISOs/RTOs and consumers. Energy managementsystem 110 also provides a simplified and/or automated process ofmanaging the energy assets in any energy-consuming and/orenergy-producing environment, such as, but not limited to, a group ofenergy assets and/or a building or group of buildings. Energy managementsystem 110 is used to facilitate an energy management service tocustomer sites 120 of energy management environment 100.

The optimized operating schedule for the chosen period of time is storedat energy management system 110 and then transmitted (i.e., deployed) tothe monitoring and control system 122 of the customer site 120. Theenergy assets 126 are then operated and/or controlled according to theoptimized operating schedule. In the deployment process, the process ofan end-user 128 of a customer site 120 accepting an optimized operatingschedule from energy management system 110 may be iterative. Once theoptimized operating schedule is deployed and in service, systemoperators 116 may use operator portal 118 to monitor and/or control theoperation of the energy assets 126 of customer site 120. Likewise,end-users 128 may use customer portal 130 to monitor and/or processinformation about the operation of the energy assets 126 of customersite 120. Additional details of an example of a process of creating andoptimizing a schedule for managing energy assets of any energy-consumingand/or energy-producing environment are described with respect to FIG.21.

FIG. 24 illustrates a block diagram showing additional details of energymanagement system 110 of FIG. 23. For example, FIG. 24 shows energymanagement environment 100 of FIG. 23 implemented in a service-orientedarchitecture (SOA). In the service-oriented architecture shown in FIG.24, the server side of energy management environment 100 is energymanagement system 110 while the client side of energy managementenvironment 100 may include any number of customer sites 120, such ascustomer sites 120-1 through 120-n.

In one example, energy management system 110 may include certainfunctional components and/or modules that are installed and executing inmemory 114 and managed by the one or more processors 112. Examples offunctional components and/or modules of energy management system 110 mayinclude, but are not limited to, certain core SOA components 210, aschedule builder module 212 that feeds an optimizer 214 (e.g.,optimization software), a markets module 216, and a database 218. Energymanagement system 110 also includes a supervisory control and dataacquisition (SCADA) system 220 as well as various servers 222 forsupporting the SOA. The functional components and/or modules of energymanagement system 110 may communicate via an enterprise service bus 230.Enterprise service bus 230 manages “calls” in and out of energymanagement system 110. A set of adaptors (not shown) may be connected toenterprise service bus 230 for interfacing with any entities that areoutside of energy management system 110, such as, but not limited to,the monitoring and control system 122 of any customer sites 120, energyregulatory agencies 150, ISOs/RTOs 152, rules information 154, energyprice information 156, weather information 160, emissions regulatoryagencies 162, and emissions information 164. The adaptors (not shown)are connected to enterprise service bus 230 for handling variouscommunication protocols.

Energy management system 110 also includes a web interface 232. Webinterface 232 may be, for example, any web browser that allows energymanagement system 110 to be accessed via a URL address. In addition tousing enterprise service bus 230, the monitoring and control system 122of any customer sites 120, energy regulatory agencies 150, ISOs/RTOs152, rules information 154, energy price information 156, weatherinformation 160, emissions regulatory agencies 162, and emissionsinformation 164 may communicate with energy management system 110 viaweb interface 232. Additionally, the one or more operator portals 118and customer portals 130 may communicate with energy management system110 via web interface 232.

To integrate diverse monitoring and control systems 122 with energymanagement system 110, a physical connection from the monitoring andcontrol systems 122 to network 170 is required. Therefore, a gateway 121may be associated with each monitoring and control system 122 ofcustomer sites 120. Gateway 121 may be any translation device betweenthe output of energy management system 110, which is using a specifiedprotocol, and one of any number of different BMS or BAS protocols and/ordifferent energy asset device protocols. Accordingly, gateway 121 isinstalled at the physical location of the monitoring and control system122 of a customer site 120. The presence of gateway 121 with eachmonitoring and control system 122 allows energy management system 110 tobe substantially customer-agnostic by connecting common networkprotocols, such as, but not limited to, LONWORKS® (Echelon Corporation,San Jose, Calif.), BACNET® (ASHRAE, Atlanta, Ga.), and MODBUS® (ModbusOrganization, Inc.), along with many proprietary network protocols.

In another example, optimizer 214 may use energy price information 156to indicate to customer sites 120 to charge batteries at times of daywhen energy prices are lowest and to utilize (discharge) the batteriesat times of day when energy prices are highest; again, reducing energycosts of a customer site 120.

In yet another example, optimizer 214 may use energy price information156 to generate optimized operating schedules for energy assets 126 thatmay be used to indicate to end-users 128 at customer sites 120 when tobid into any of the various wholesale electricity markets, such as (1)the energy market, (2) the day-ahead scheduling reserve market, (3) thecapacity market, (4) the synchronized reserve market, and (5) theregulation market.

With respect to emissions information, optimizer 214 may use emissionsinformation 164 to generate optimized operating schedules for energyassets 126 that may be used to indicate to end-users 128 at customersites 120 opportunities to buy and/or sell emissions credits. In thisway, customer sites 120 may have opportunity to reduce emissions and/orreceive revenue from emissions trading.

Markets module 216 may be a software component of energy managementsystem 110 for interacting with any entities in the energy markets, suchas ISOs/RTOs, any/or with any entities in the emissions trading markets.For example, markets module 216 may include certain market interfaces,which may be any mechanisms for interfacing with the market. Further,markets module 216 may include a registration component that is used forregistering energy assets in a particular market for committing assets.Additionally, markets module 216 may include a markets bidding componentthat is used to submit bids into certain energy markets. Additionally,markets module 216 may include an energy markets settlements componentthat is used to process settlements in any energy markets between, forexample, ISOs/RTOs 152 and customer sites 120. Markets module 216 mayalso include an emissions market settlements component that is used toprocess settlements with respect to emissions trading, such assettlements between emissions regulatory agencies 162 and customer sites120.

SCADA stands for supervisory control and data acquisition. It generallyrefers to industrial control systems, such as computer systems thatmonitor and control industrial, infrastructure, and/or facility-basedprocesses. In energy management system 110, SCADA system 220 may be usedto communicate directly with customer sites 120. SCADA system 220 mayalso communicate with other components of energy management system 110via enterprise service bus 230. SCADA system 220 handles, for example,ancillary services, such as “regulation” and “synchronized reserve.”

FIG. 25 illustrates a block diagram of schedule builder module 212 ofenergy management system 110 of FIG. 24. Schedule builder module 212 mayinclude any components for processing any information that may be usefulwith respect to creating an optimization case 310 with respect to energyassets. For example, schedule builder module 212 may include a customercomponent 312 for processing customer information (e.g., customer site120 information), a location component 314 for defining and/orconfiguring the customer's location, a supply contract component 316 forprocessing supply contract information, a strategy component 317 forprocessing optimization strategy information, a price component 318 forprocessing energy price information 156, a weather component 320 forprocessing weather information 160, a solar gain component 322 forprocessing solar gain information, and/or an emissions component 324 forprocessing emissions information 164.

Customer component 312 may process customer information (e.g., customersite 120 information), such as, but not limited to, customer name andcontact information, customer type information (e.g., a university, abusiness, a retailer, a hospital, a factory), building (s) occupancyinformation, miscellaneous load information (e.g., lighting, anyelectric powered equipment), and the like.

Location component 314 may be used for defining and/or configuring thecustomer's location. The definition of a customer's location is notlimited to a geographic address. Rather, the customer's location may beany configuration of energy assets, buildings, and/or geographiclocations. For example, the customer's location may be configured as onebuilding; a groups of buildings; one energy asset; a group of energyassets; one energy asset for one building; one energy asset for multiplebuildings; one monitoring and control system 122 per building; multiplemonitoring and control systems 122 per building; one monitoring andcontrol system 122 for multiple buildings; energy assets and/orbuildings in one town, city, or state in combination with energy assetsand/or buildings in another town, city, or state; and the like.Additionally, groups of energy assets or buildings may be in closephysical proximity to each other or may be physically distant and evenseparated by time zones.

Supply contract component 316 may be used to process supply contractinformation, which is the service contract between the ISOs/RTOs 152 andcustomer sites 120.

Strategy component 317 may be used to process optimization strategyinformation. For example, the optimization strategy may be determined bythe definition of a customer's location per location component 314. Thatis, the grouping of energy assets and/or buildings may determine thebest optimization strategy for the case. Example strategies include, butare not limited to single optimization, iterative optimization, customerbaseline (CBL) optimization, parametric estimation optimization, and soon.

Price component 318 may be used to process energy price information 156.For example, price component 318 may query ISOs/RTOs 152 for energyprice information 156 with respect to any energy market in a certaingeographic region, such as the day-ahead energy market and the real-timeenergy market.

Weather component 320 may be used to process weather information 160.For example, weather component 320 may query weather information 160 forcurrent and/or predicted temperature and/or cloud cover information fora certain geographic location per location component 314. Per weathercomponent 320 and/or weather information 160, optimizer 214 may factorin the delta between the outside temperature and inside temperature of abuilding or group of buildings. Additionally, per weather component 320and/or weather information 160, optimizer 214 may factor in the amountof cloudiness with respect to solar gain calculations.

Solar gain (also known as solar heat gain or passive solar gain) refersto the increase in temperature in a space, object, or structure thatresults from solar radiation. The amount of solar gain increases withthe strength of the sun, and with the ability of any interveningmaterial to transmit or resist the radiation. With respect to schedulebuilder module 212, solar gain component 322 may process solar gaininformation about one or more buildings of customer site 120 perlocation component 314. For example, building-specific solar gaininformation, which is based on an analysis of the customer's buildingand/or buildings and weather information per weather component 320. Thatis, solar gain is determined by the direction and orientation ofwindows, direction and orientation of the sun (by day of the year), andamount of cloud cover. For example, the solar gain may be lowest whencloudy and highest when not cloudy, and anything in between. Again,optimizer 214 may factor in the amount of cloudiness with respect tosolar gain calculations. Other factors of solar gain include buildinginformation (e.g., size, mass, type and thickness of building materials,R-factor, etc) and window information (e.g., type, size, thickness,direction, R-factor, etc).

Emissions component 324 may be used to query emissions regulatoryagencies 162 for emissions information 164 with respect to any emissionsmarket in a certain geographic region. For example, emissions component324 processes emissions cap information, cost information for buyingemissions credits, and/or price information for selling emissionscredits for the geographic region of a certain customer site 120.

Optimization case 310 also includes defaults data 330, forecasts data332, constraints data 334, and readings data 336. For each individualenergy asset 126 of each customer site 120 a set of default settings isestablished, which is defaults data 330. For example, there is a setdefault settings for a certain HVAC unit, another set default settingsfor a certain chiller, another set default settings for a certainheater, and so on; all included in defaults data 330. Defaults data 330may be considered static data. This is because the default settingsremain substantially the same from day to day for a given energy asset.

For each individual energy asset 126 of each customer site 120 there isalso a set of forecast settings, which is forecasts data 332. That is,forecasts data 332 includes forecast operating values for eachindividual energy asset 126. The forecasts data 332 is set up by thesystem operator 116. The forecasts data 332 may initially includedefault data and/or historical data. A system operator 116 may modifythe initial contents of forecasts data 332 based on any currentconditions. Forecasts data 332 may include a schedule, such as forecastdata for every 15, 30, 45, or 60 minutes for a chosen period of time.Forecasts data 332 may be considered time series data. This is becausethe data may change over a time period.

In one example, the forecast values in forecasts data 332 are used tomodify the default values in defaults data 330 and, thereby, achieveimproved optimization with respect to reducing energy costs and/orgenerating revenue from the day-ahead and/or real-time energy markets.The forecast values in forecasts data 332 may cover the whole day or anyportion of the day. For example, forecast values may be used betweennoon and 6 pm and the default values may be used for the rest of theday. In the optimization process performed by optimizer 214, a firstpass of the process may be to optimize using the default settings indefaults data 330. Then a second pass of the process may be to optimizeusing the forecast settings in forecasts data 332.

Constraints data 334 may include any information for constraining theoperation and/or use of energy assets 126 of a certain customer site120. One example of constraints may be any constraints that are on aparticular energy asset 126, such as minimum run time or startup time(e.g., ice making) Another example of constraints may be constraintsabout the availability of a certain energy asset 126, such asinformation from the customer that a chiller is going to be down between2 pm and 4 pm or that the chiller is running at only 50% capacity. Yetanother example of constraints may be the allowable minimum and maximuminternal temperatures of buildings.

Readings data 336 are the actual readings from the customer's monitoringand control system 122. For example, readings data 336 may be meterreadings, thermostat readings, any energy assets readings. When creatinga case, system operators 116 may pull any useful readings from readingsdata 336 and use these values as a baseline value into the optimizerFurther, system operators 116 may monitor actual real-time readings.Then based on actual readings from readings data 336, system operators116 may make certain adjustments to any energy asset 126 and then rerunthe optimization. Additionally, meter readings in readings data 336 maybe used for parametric estimation.

Schedule builder module 212 may also include a case modeling andsimulation component 340 for processing any information about buildingsand/or energy assets of a customer site 120 and then models andsimulates the environment. That is, case modeling and simulationcomponent 340 is used to simulate a model of energy assets 126 and theiroperation in a given environment over a chosen period of time givencertain input information and/or variables. For example, case modelingand simulation component 340 may include a building input 342 forreceiving any type of building information (e.g., location, size, mass,# of floors, type and thickness of building materials, R-factor, etc)and/or building zones information (e.g., zone 1=floor 1, zone 2=floor 2,zone 3=floor 3, etc). Additionally, case modeling and simulationcomponent 340 may include an energy assets input 344 for receiving anytype of information about energy assets 126, such as, but not limitedto, any operating specifications and/or attributes of, for example, HVACassets, chillers assets, heaters assets, energy storage assets (i.e.,thermal and electric storage), energy generation assets, and/orrenewable energy assets.

The aforementioned information about energy assets 126 may originatefrom a customer (i.e., customer site 120). That is, the attributesand/or technical specifications of each energy asset 126 may be suppliedby the customer site 120 and initially entered manually. However, inother examples, a simulation components library of predefined assets mayexist that provides a simulation model of any types, brands, and/ormodels of energy assets. In this example, the customer may supply onlythe types, brands, and/or models of its energy assets and then casemodeling and simulation component 340 may pull the information from thesimulation components library when building an optimization case 310. Inone example, the simulation components library includesindustry-standard XML representations of any types, brands, and/ormodels of energy assets 126.

Using the aforementioned information and/or variables, case modeling andsimulation component 340 creates a model of the customer site 120 (e.g.,a building or group of buildings per location component 314 andassociated energy assets 126) and simulates the environment at thecustomer site 120 for a chosen period of time. Schedule builder module212 generates a case snapshot file 350 that contains a snapshot of thesimulation results from case modeling and simulation component 340 andall other information collected by and/or included in optimization case310. Case snapshot file 350 is fed into optimizer 214. Optimizer 214uses a two step process to process the information in case snapshot file350 and generate a solution set in the form of an optimized schedule352. The optimized schedule 352 is for a chosen period of time, such asone 24-hour period (i.e., one calendar day), and includes settingsinformation on a predetermined interval, such as every 15, 30, 45, or 60minutes. The optimized schedule 352 from optimizer 214 is returned toschedule builder module 212. Schedule builder module 212 may then deploythe optimized schedule 352 to a customer site 120.

Following is a description illustrate how the operation of any of theenergy assets described herein can be implemented in different markets.

Dynamic Virtualization

Dynamic virtualization is an integrated solution for energy generationand storage involving energy assets, such as batteries and solargenerators. This uses a version of examples with virtual partitioning ofan energy storage asset. Dynamic virtualization can be used toco-optimize energy storage assets and solar generation across differentenergy markets or other uses. These markets or uses may include (1)electric energy provided over the grid to the energy market, and (2) theancillary services market (which may include regulation, which isfocused on regulation of power frequency and voltage on the grid) or (3)use of the storage device to maintain power quality at the owners'facilities.

Dynamic virtualization uses examples of systems with the virtualpartitioning of the battery or other type of energy storage asset intovirtual separate batteries, each virtual energy storage asset beingallocated to separate markets or functions, such as participating in theenergy market, and the ancillary services (regulation) market or use tomaintain power quality at the premise. The virtual partition of thebatteries is not physical, but is instead an allocation of energystorage asset capacity to various markets or uses. This virtualpartition by allocation is dynamic in that it can be constantly changedin response to changing price points and performance requirements duringthe day.

There are rapid swings in load on the spot electric energy market. Inorder to maintain electrical balance on the grid and regulate consistentpower and voltage on the grid over short periods of time, for example,over periods of four seconds, fifteen seconds, or one minute, the gridoperator sends out signals to change generation to match the loadchanges. Batteries are particularly well suited to respond to theseshort response time signals.

With examples of the principles herein, energy storage assets such asbatteries can be applied to swing between the markets for energy andancillary services for regulation of the grid or for the maintenance ofpower quality at the energy storage asset owner's facility. In the past,batteries were not purchased and installed for the purpose of providingregulation services, because batteries tend to be too expensive for thispurpose alone. Most regulation services now come from gas poweredgenerators providing about 1-10 megawatts, and these energy assets taketime to turn on and off. Industrial batteries, however, are instant onand off and usually provide power in the 1 megawatt range—and canrespond to grid operator signals in milliseconds.

In the past, energy storage and energy storage asset facilities wereusually purchased with the intent to provide backup power for theowners, in case the electric power grid goes down or temporarilyprovides inadequate power. However, once the battery or other type ofenergy storage assets are installed to satisfy backup capacity for theowner, they may also to some extent be active in the regulation marketto regulate the power and voltage on the grid, and in the energy market,to sell power into the grid in response to real-time pricing changes (orto cut the user's demand on the grid). For example, energy storageassets may discharge to the grid during high LMP price hours.

Energy storage assets may include batteries, ice units, compressed air,or other technologies to store energy on site by users and generators ofpower. Batteries may be of any type, including lithium ion, lead acid,flow batteries, dry cell batteries, or otherwise.

Solar generators of power may include solar panels, solar cells, anyother photovoltaic power generator, or any means for generating powerfrom sunlight. This may also include generation of electricity fromsteam or similar use of liquid to gas phase generation from sunlight, togenerate electricity.

The energy market involves generating power, distributing power into thegrid, and drawing power out of the grid, each at a price. This ismeasured in terms of megawatt hours that are the amount of powerdelivered. Energy is delivered for sustained periods of time, such asfor 15 minutes or more.

The capacity market is measured in terms of megawatts of capacity. Inthis market, a seller makes their facilities available to generateelectricity when needed and holds them in reserve for that purpose, butmay never actually distribute energy into the grid rather than just beon-call. This, in effect, pays the seller to be available and impactsthe reliability of the grid.

The ancillary market includes regulation of frequency and voltage in thegrid, and the provision of an operating reserve. The regulation of thevoltage in the grid involves discharging energy into the grid orabsorbing energy from the grid in small increments, frequently, forshort periods of time, and very rapidly.

Smart grid services increasingly rely on new technologies such asrenewable energy and large-scale storage resources. Unfortunately, thelife-cycle costs associated with such resources, when takenindividually, are still high compared with more traditional forms ofenergy production. In addition, the desired proliferation of distributedand renewable resources on the power grid introduces new threats to itsreliable operation, as they are subject to unpredictable drops inoutput, such as when the wind stops blowing. Consequently, both economicand reliability issues introduce substantial obstacles to a highpenetration of those technologies in the power grid.

By themselves, storage resources such as electrical batteries arepresently high cost options. Likewise, photovoltaic generation and windturbines are comparatively quite expensive and their intermittencycreates new strains on the power grid.

However, when optimally managed by various examples disclosed herein toprovide timely support to the power grid, the net cost of electricalstorage can be substantially reduced, as the result of payments by thegrid operator (ISO/RTO) provides for facilities that can be called on toprovide such support. Also, combining energy storage with intermittentgeneration makes technologies such as wind and solar more predictable onthe grid, and hence, more valuable.

Examples, including dynamic virtualization, can dramatically improve theeconomics of renewable generation and storage technologies, byco-optimizing their operation to participate in the various energy andancillary services (including regulation) markets and thus maximizetheir economic benefits.

Examples focus on the economics of batteries and energy storage and, byproviding energy resource optimization and a gateway to the wholesalemarkets, can help facility managers deploy a comprehensive energystorage solution that can cost-effectively meet an organization'sbusiness objectives.

More broadly, when optimally coupling energy storage with renewablegeneration, various examples redefine the economics of such resources,while providing firm, dispatchable virtual generation that supports thereliability objectives of the power grid. Thus, by integratingdistributed resources into virtual generation via system operatordispatch, examples can help enable the acceleration of renewable energygeneration technologies such as solar and wind.

Systems Including Energy Storage Assets

Large-scale storage is widely seen as a necessary piece of the smartgrid and a key component of America's electricity future. Thisrecognition is driven by the following factors: (1) the growing adoptionof intermittent renewable power sources; (2) state and nationwide budgetshortfalls, leading local governments to seek cost-effective solutionsfor maintaining America's aging infrastructure; and (3) the widespreadbelief that electric vehicles (“EVs”) will materially grow their marketshare over the next 5 to 15 years.

In this context, stakeholders have been looking for ways to acceleratethe development and implementation of grid-level storage. Effectivebattery and other energy storage asset solutions can take unpredictableenergy resources and turn them into reliable power, while matchingelectricity supply to demand; they play a crucial role in fosteringmicrogrids and distributed generation, viable alternatives to expandingthe U.S.'s power infrastructure; and they can address the new and uniqueconcerns created by EVs, such as helping to maintain grid stability andgiving utilities and grids more control over energy dispatch.

A key concern with batteries has long been their high upfront cost andlong payback periods. Various examples address this by providingbattery-owners a robust gateway to the wholesale electricity markets,thus unlocking new streams of revenues that increase their return oninvestment. This may also apply to other types of energy storage assets.

Various examples provide processor-executable instructions (includingsoftware solutions) that optimizes participation in wholesale markets byproviding energy storage asset owners with dynamic virtualization, aservice that continuously re-partitions the energy storage asset fordifferent markets and uses, chiefly real-time energy, and regulation,and power quality control, in an optimized manner, based on pricing andweather data, retail electricity rates, and characteristics of theenergy storage asset and its host site.

For large retailers and supermarkets, backup generation is a necessarybut often expensive proposition. The nation's largest big box chainshave taken a variety of approaches to minimizing the costs of providingsubstitute power in the case of an emergency or brownout; but for manystores, their only choice to date has been inefficient and costly dieselgenerators.

Examples with dynamic virtualization optimally manage an energy storageasset's state of charge based on the revenue producing opportunities inthe wholesale market, as well as the organization's business objectives,such as providing backup power to critical loads for a given period oftime. Thus, when paired with these examples, the energy storage assetbecomes an energy resource that will concurrently: (1) participate inthe energy markets by providing a way to shift the net load of afacility from high- to low-price periods; (2) participate in thefrequency regulation market by responding to real-time signals from thegrid operator; (3) participate in other wholesale markets, such asenergy and synchronized reserve; and (4) provide reactive/voltagesupport to the microgrid/distribution grid.

Examples enable the energy storage asset to maximize revenues from thevarious wholesale markets, while maintaining its ability to achieve itsmain objective of providing a reliability service to the organization.To achieve this, examples herein describe virtualization of the energystorage asset and creating dynamic “energy storage asset partitions,” ina manner similar to the way computing resources are virtualized. Throughits optimization capability, an example determines in hourly incrementswhich portion of the controller output (including its capacity), andhence the energy storage asset capacity (including its SOC), can beallocated to the energy and regulation markets respectively, whilemaintaining sufficient reserve to meet the forecasted backuprequirements. The optimal control (to perform the optimization describedherein) can take into account the forecasted and real-time hourly pricesfor each of the markets, along with the time and weather dependentbackup requirements of the facility. When combined with other resourcessuch as renewable generation, backup generation or demand response, theexamples described herein can extract the maximum value of all suchresources while meeting the organization's reliability, comfort, andsustainability objectives.

In energy markets, ancillary services support the reliable operation ofthe transmission system as it moves electricity from generating sourcesto retail customers. Examples of ancillary services include“Synchronized Reserve” and “Regulation.” Synchronized Reserve supplieselectricity if the grid has an unexpected need for more power on shortnotice. Regulation is a service that corrects for short-term changes inelectricity use that might affect the stability of the power system. Anentity that is participating in the Regulation service must be able torespond rapidly (within a few seconds or minutes) to a “regulation”signal.

The different types of energy assets available at a customer site candetermine the types of energy markets in which the customer site mayparticipate (e.g., capacity market, energy market, or synchronizedreserve market). For example, the capacity of a backup generator,battery and HVAC can be committed for economic demand response or acapacity market, according to any of the principles described herein. Inanother example, the capacity of a battery or a computing loadmanagement system can be committed to a regulation market, according toany of the principles described herein. As another example, the capacityof a backup generator and a battery can be committed for a demandresponse, according to any of the principles described herein. As yetanother example, the capacity of a battery and a HVAC can be committedfor power quality, according to any of the principles described herein.

Following is a description of the different types of markets, includingenergy markets and regulation markets, to illustrate how the operationof any of the energy assets described herein can be implemented in eachmarket.

Regulation Market

In a non-limiting example, capacity of the energy storage asset may becommitted to the regulation market to maintain the frequency and/orvoltage on the power line. For example, system operators seek tomaintain the system frequency at very near to a nominal frequency ofaround 60 Hz in the U.S. or around 50 Hz in some other countries(including countries in the European Union). If the frequency is toohigh, there is too much power being generated in relation to load. Asystem operator would send a signal to participants in the regulationmarket to increase their load, or ask for generation to be reduced, tokeep the system in balance. If the frequency is too low, then there istoo much load in the system, and the system operator would send a signalasking for generation to be increased or the load reduced. A gridoperator may use a real-time communication signal to call for either apositive correction (referred to in the industry as “regulation up”) ornegative correction (referred to as regulation down”). If load exceedsgeneration, the frequency and voltage tend to drop. The ISO/RTO systemoperator would relay a signal requesting regulation up. If, however,generation exceeds load, the frequency tends to increase. The ISO/RTOsystem operator would relay a signal requesting regulation down(including asking for reduced generation).

The regulation market may seek commitment of a system on an hourlybasis. However, the ISO/RTO system operator may relay regulation signalsfor regulation up and/or regulation down at much shorter timescales. Forexample, during the commitment period, the adjustments of regulation maytake place minute-by-minute, on the order of a minute or a few minutes,or on the order of a few seconds (e.g., at 2-second or 4-secondintervals). Traditional regulation applies to slower responding energystorage assets (e.g., assets with about 5 minutes response time), suchas but not limited to chillers. Faster responding energy storage assets,such as but not limited to batteries, can respond within about 2seconds. In an example, the objective cost function may include a termto performance incentives offered for fast responding energy storageassets. To participate in the regulation market, a resource may receiveand may need to respond to a regulation signal generated by the gridoperator approximately every 2 seconds. (In some territories, this rulemay be relaxed somewhat for batteries.) The energy storage assetresponds to this signal with a percentage of its maximum resourcecapability that is bid into the regulation market. Examples receive andrespond to this signal and distribute it among the various resourcesparticipating in the regulation market within a given price zone, basedon the results produced by an optimizer

If the ISO/RTO system operator sizes the regulation signals toadequately balance the signal in the long run, the charge of the energystorage asset may merely fluctuate around its initial state of chargewhen it started to provide regulation. That is, the proportion of theavailable state of charge of the energy storage asset that is committedfor use to provide regulation may be delivered at variable charge ratesor discharge rates. Adequately balanced regulation signals shouldneither completely deplete nor fill the energy storage asset.

In a non-limiting example, the regulation price may be set at averagevalues of around $30-$45/MW per hour, with hourly rates fluctuatingaround this average value. Some regulation markets may pay simply forthe commitment of an available capacity of the energy storage assetduring a time period, such as for an hour, with a separate payment forthe total amount of energy ultimately provided. Thus, payment at theregulation price may be made for the period of commitment, even if thesystem is not called upon to provide regulation during the commitmentperiod.

There may also be additional payment from the energy market for energygenerated, based on the wholesale electricity market price (the LMP).

Operating characteristics of the energy storage asset include power (orits instantaneous delivery capability in kW) and the energy stored inthe energy storage asset (or the amount of power it can generate overone hour, or kWh). In a non-limiting example, a battery rated at 1.5 MWpower and 1.0 MWh energy storage capacity will be able to provide 1.5 MWpower for a total period of 40 minutes (60×1/1.5). Thus, if the ownerbids 1.5 MW into the regulation market for a given hour, a 50% dischargesignal over 2 seconds could decrease the battery's charge level by 0.8kWh (1.5 MW×1/1800 hrs).

As part of a certification for participating in the regulation market,the ISO/RTO system operator may verify that the energy storage asset iscapable of responding to the regulation bid into the market. The ISO/RTOsystem operator may require that the energy storage asset be able to becharged/discharged at its full enrolled amount, when receiving a +/−100%regulation signal within a duration of 10 minutes. In the 1.5 MW exampleabove, the battery charge would be increased/decreased by +/−250 kWh(1.5 MW×1/6 hr).

For example, assuming that the energy storage asset starts with aninitial state of charge of 50% at time t=0. Ideally, the regulationsignal is “net zero,” meaning that the quantity of charged/dischargedenergy averages to zero over a given 24-hour period. In reality, thestate of charge of the energy storage asset may at times drift to thelimits of the energy storage asset's recommended state of charge. If thestate of charge exceeds some adjustable maximum or minimum values,various examples include compensating by exiting the regulation marketfor the next hour and bringing the energy storage asset back to itsinitial set-point.

In an example, the operating schedule that is generated according to animplementation of an apparatus herein specifies intervals of time whenthe energy storage asset may be committed to the regulation market.During these time periods, the operating schedule may additionallyindicate the points during these intervals of time where energy may bebought to charge the energy storage asset if its state of charge fallsbelow a desirable limit, or where excess energy may be sold if the stateof charge is too high. This discharge can contribute to a short-termdemand response action in the real-time energy market.

Energy Market

To participate in the energy market, the energy storage asset should tobe able to provide the “as bid” energy into the real-time market for thenext hour. Various examples compute the optimal charge or dischargesignal in anticipation of or in response to the economic signals, whilemaintaining minimum and maximum constraints on the state of charge ofthe energy storage asset. When combined with other controllableresources, such as renewable generation or advanced lighting and HVACsystems, examples extract the maximum economic value of each resource,given external factors and constraints. For example, examples can use anenergy storage asset to compensate for the intermittency of renewablegeneration, and can include demand response actions to help maintain thebalance.

FIG. 26 shows an example energy storage asset optimization in responseto economic signals and performance needs. The horizontal axis is timeover a 24 hour cycle. The left vertical axis is megawatt hours. Theright vertical axis shows price in dollars per megawatt hours. Thevolume under the line battery 810 shows the stored capacity in thebattery. The three lines below the horizontal axis shows the discharge820 from the battery. The seven vertical lines 830 above the horizontalaxis shows charging to the battery 830. The line 840 shows the LMPenergy price throughout the 24-hour cycle to which indicated energyassets are responding. In this example, examples determine the optimizedhourly charge and discharge schedule of a 1.5 MW/1.0 MWh battery inresponse to an LMP price signal. The optimization is further constrainedto maintain a 200 kWh minimum capacity for backup purposes, and amaximum capacity of 800 kWh to maintain charge/discharge cycleefficiency.

Spinning Reserve Market

To participate in the spinning reserve market, the energy asset shouldto be able to commit building asset resources to provide power duringunplanned outages of base load generators. Spinning reserve isgeneration capability that can provide power to the grid immediatelywhen called upon by the ISO/RTO and reach full capacity within 10minutes. The energy storage asset included in the building asset needsto be electrically synchronized with the grid, e.g., through thecontroller, to participate in this market. Revenue in the spinningreserve market is for capacity rather than energy. It requires quickresponse but makes low total energy demand. Requests in the spinningreserve market may be made around 20-50 times per year.

Revenue for the spinning reserve market may be determined based on theability of an energy storage asset to provide power during an unplannedevent, such as a generator failure. Revenue may also be derived based onthe amount of energy (MWh) that is generated during active participationin the spinning reserve market, such as based on the electricitywholesale price.

Market Based On Voltage/VAR Ancillary Service

To participate in a market based on a voltage/VAR ancillary service,certain resources of the energy asset may be committed to provide forvoltage control and/or VAR control.

The voltage/VAR ancillary service seeks to maintain reliability andpower quality. It may appear at the microgrid level or feeder level of adistribution system.

A voltage control ancillary service assists in maintaining systemvoltages within an acceptable range (120 volts ±about 5% or 220 volts±about 5%) to customers served by a feeder. For example, if the supplyline voltage fluctuates by some amount, resources of the energy assetmay be committed to adjust the distribution primary voltage so that thedistribution primary voltage also does not drift out of the acceptablerange. In another example, if the current (ampere) flowing on the feederincreases during peak load conditions, the voltage along the feeder maydecrease due to an increase in current flow, resulting in decreasedvoltage for customers that are further from the substation end of thefeeder. Here, resources of the energy asset may be committed to raisethe line voltage under peak load conditions to account for any increasedvoltage drop. If instead the feeder is lightly loaded, the voltage dropmay be lower, and resources of the building asset may be committed tolower the voltage to avoid possible high voltage conditions.

VAR refers to the reactive power (measured in volt-ampere reactive(VARs)). VAR is the electrical energy that energizes capacitivecomponents and inductive components in a power system. A non-limitingexample of a capacitive component is overhead conductors, which arecontinuously charged and discharged by an alternating current (AC)waveform. Non-limiting examples of inductive components are electricmotors and transformers, which can store energy in magnetic fields thatare used for device operation. By reducing the amount of VARs flowing onthe distribution feeder, an electricity supplier can reduce electricallosses and improve the voltage profile along the feeder. Where reactivepower varies throughout the day, the capacitive components of a energyasset that are equipped with switches can be placed in or out of serviceas needs vary during the day. These capacitive components of the energyasset may be equipped with controllers. A system, apparatus, or methodmay be used to determine when to switch the switches on or off. Forexample, when the voltage at the location of the capacitive component islow, the operating schedule determined according to a principle hereinmay include instructions for the controller to close the switch to placethe capacitive component in service. When the voltage is high, theoperating schedule may include instructions for the controller to openthe switch to remove the capacitive component from service.

Revenue from a market based on the voltage/VAR ancillary service may bedetermined based on the ability of an energy storage asset of the energyasset(s) to be used to provide the voltage controls and/or the VARcontrols. In an example, the voltage/VAR control may apply in amicrogrid application at the microgrid bus level, which may introduce areliability cost to the computation of the net-energy-related cost.

Co-optimization Across Multiple Markets and/or Ancillary Services

As described above, the economic signal can be a driver for the averagecharge status of the energy storage asset. It responds to price signalsthat are averaged on an hourly basis. The regulation signal can be seenas having a “bias” effect over the average charge, in response to theregulation commands. Examples co-optimize the energy storage assetcharge by first economically optimizing the charge status of the energystorage asset, then allocating the balance of the available power to theregulation market, on an hourly basis.

By adding user-adjustable upper and lower constraints to the optimizedenergy storage asset charge, examples take into account reliabilityobjectives (e.g. backup) and charge/discharge cycle efficiency. Otherconstraints can be added, based on the type of energy storage assettechnology used, to maximize charge/discharge round trip efficiency, andoptimize energy storage asset life versus energy storage assetreplacement costs.

In addition to co-optimizing a storage resource at a given location,examples have the capability to perform a global optimization acrossmultiple customers within the same price zone, and disaggregate theregulation and economic signals among the various customers. Inparticular, this gives customers that do not have the minimum energystorage asset capacity required the ability to participate in theregulation market.

Co-optimization With Other Distributed Resources

With various examples, distributed resources can earn maximum economicbenefit through co-optimization. Co-optimization of various resources onone site results in accelerated payback for all assets, and this, inturn, accelerates the market-wide penetration of these resources.

FIG. 27 shows an example generation schedule for battery-photovoltaicco-optimization. FIG. 27 shows an example where the same battery used inthe previous example in FIG. 26 is combined with 0.5 MW of PV(solar-photovoltaic) generation. The horizontal axis shows the time inthe 24-hour cycle. The left vertical axis shows megawatt hours. Theright vertical axis shows price in dollars per megawatt hours. The load910 is the electric load on the facilities. The import of power 920shows the power imported into the facilities from the grid. The battery930 shows the three bars below the horizontal axis for the powerdischarge from the batteries at specific times. The diesel 940 is notshown because diesel generation is not used in this co-optimizationbecause of its relative price. The solar 950 shows the power used by thesystem and/or stored in the batteries from the solar generator orphotovoltaic generator at various times. The LMP line 960 shows thefluctuating price for electricity during the 24-hour cycle.

Example Energy Storage Assets

Various examples are technology agnostic and can optimize any storageinstallation. However, certain forms of storage, such as compressed airand ice storage, are currently not recognized as applicable resourcesfor some regulation markets.

Aided by significant private investment, grid-scale batteries havesignificantly reduced in cost over the past decade. Differenttechnologies appear to have converged around a similar price: withbatteries offered at roughly $1-2 per Watt, and $1-2 per Watt-hour,before Balance of Plant (“BoP”) costs. (Watts [W, kW, MW] are a measureof power, i.e., the charge and discharge rate of an energy storageasset. Watt-hours [Wh, kWh, MWh] are a measure of energy, i.e., thestorage capacity of an energy storage asset.) At these prices, energystorage asset owners and lessees can use examples to achieve a positivereturn over the installed life while meeting their sites' backup needs.

Below is a brief overview of each different types of energy storageassets:

Lithium-Ion Battery

This “power battery” is well-suited for regulation with high efficiencyand hybrid opportunities. However, it has a high cost and little dataexists to corroborate lifespan claims

Quoted prices include $2 million for a 1 MW/1 MWh unit, and $1.5 millionfor a 1 MW/250 kWh unit.

Lithium-Ion (Li-Ion) batteries are receiving great attention becausethey are the preferred battery for electric vehicles. Presently, Li-Ionbatteries are among the most expensive of the storage options available.This may change, as many companies are pouring resources into new Li-Ionvariants; however, some suggest that the chemical characteristics ofLi-Ion cells make it difficult to significantly reduce their cost.Additionally, Li-Ion is a new technology so that no company hasempirically demonstrated Li-Ion's lifespan. Companies have tried toallay these concerns through “accelerated testing” that charge/dischargethe battery more rapidly, but this does not provide full insight intohow well Li-Ion batteries perform over time.

Li-Ion batteries are very dense and therefore very small compared toother technologies. One manufacturer's 1 MW/1 MWh unit, for example, hasdimensions of 8′×20′. In comparison, a quoted lead-acid unit withsimilar specs has dimensions of 40′×70′.

Hybrid opportunities for Li-Ion batteries are discussed in the flowbattery section.

Lead-Acid Battery

This battery is the lowest-cost option with long lifespan and proventechnology. However, it is physically large with high maintenance andlimited depth of discharge.

Quoted prices include $896,000 for a 1 MW/2 MWh unit, and $512,000 for a1 MW/500 kWh unit.

Lead-Acid batteries, which have the same chemistry as a car battery, areproven for long-lasting grid applications. One manufacturer's 1 MW/1.4MWh unit lasted for 12 years, from 1996-2008, as both a provider ofvoltage support and a backup power source, before the battery cells werereplaced. The original power electronics of that installation stillfunction, and the unit is running with a new set of lead-acid cells.

A downside of lead-acid batteries is that they are very heavy and verylarge. This is why they are not being considered as much for EVs, andthis poses other logistical challenges for metropolitan installations.Lead-acid batteries are also considered to be high maintenance. Theyneed to be kept within a narrow temperature range, and therefore requiretheir own building (for industrial power uses), as well as periodicupkeep. Also, lead-acid batteries are typically oversized becauseexceeding the lower bounds of their state of charge can damage thecells. They are best for regulation or voltage support, and as backup ifsized explicitly for that purpose.

Flow Batteries

These batteries can be fully charged and discharged without damage tothe battery. Also, “hybridization” is possible. However, this “energybattery” limits regulation market opportunities and has low round-tripefficiency.

Quoted prices include $1.15 million for a 1 MW/1 MWh battery.

Flow batteries are energy batteries, i.e., they are best suited forbackup electricity, but their chemistry limits their ability to providehigh-MW regulation. The typically configured flow battery takes 4 hoursto charge/discharge, and flow batteries have lower round-tripefficiencies than other types (roughly 75% in contrast to Li-Ion's 90%).With flow batteries, a tank is filled with electrolyte fluid that flowsthrough solid cell stacks located at the top of the unit. The liquidsolution never degrades, but the cells need to be replaced every 5 or 6years. The cost of cell replacement is 10-15% of the total unit.

The electrochemical characteristics prohibit them from power-denseapplications, unless they are oversized and paired with a largeinverter, or “hybridized” with another battery technology. Hybridizationcan be provided by some suppliers in conjunction with a well-establishedpower electronics provider. One manufacturer has created a system thatallows its “energy” batteries to be paired with “power” batteries, likelithium-ion, connected through a single inverter. A leading lithium-ionbattery manufacturer recently announced a plan to provide a similarLi-Ion/flow battery unit for grid-scale applications.

Dry Cell Technology

This power battery is good for the regulation market. However, it hasvery small recommended depth of charge/discharge and is expensive.

Quoted prices include $1.5 million for a 1.5 MW/1 MWh battery, plus 30%extra for BoP (“Balance of Plant”).

These batteries provide high power-to-energy ratios that make themattractive for regulation, so long as they remain within a fairly narrowrange of state of charge. These batteries are not meant to fully chargeor discharge and pushing their recommended operating parameters affectstheir lifespan. Ideal state of charge is 20-80%. Because of theseconstraints, these batteries would need to be oversized to providebackup. These batteries are more expensive than cheaper options such aslead-acid.

Based on their characteristics, these batteries are likely suited forprojects whose primary objective is not backup power, but rather systemssupport. They provide high-MW regulation, can address voltage sagconcerns, and can be recharged by regenerative braking. However, whentheir state of charge limitations are taken into account, they appear tobe a costly technology, even in comparison to lithium-ion.

Ice Units

The thermal storage capacity of an ice unit can be used according to theprinciples herein as an energy storage asset.

Ice units can be used to modify how a building asset is cooled,including how energy is consumed for cooling/air conditioning. An iceunit generally consists of a thermally-insulated storage tank thatattaches to a building asset's air-conditioning system. The unit makesice (generally at night when supply costs tend to be lower) and usesthat ice during the day to deliver cooling directly to the buildingasset's existing air conditioning system. Storage tanks can be on theorder of hundreds of gallons of water (e.g., about 450 gallons) ofwater. The water is frozen by circulating refrigerant through coppercoils within or surrounding the tank. The condensing unit then turnsoff, and the ice is stored until its cooling energy is needed. Duringthe higher temperature daytime hours, the power consumption of airconditioning and demand levels on the grid, increase. The ice unit maybe used to replaces the energy-demanding compressor of a buildingasset's air conditioning unit. The melting ice of the ice unit, ratherthan the air conditioning unit, can be piped around the building assetto cool it.

Compressed Air

The storage capacity of compressed air can be used according to theprinciples herein as an energy storage asset.

For example, compressed air energy storage (CAES) technology provides away to store compressed air, using energy generated at lower cost at onetime, and use that compressed air at another time when energy costs arehigher. For example, energy generated during periods of low energydemand periods (such as during off-peak electricity usage as night) maybe released at on-peak times to meet higher demand. The CAES system maybe located where there is large, accessible air-storage pockets orcaverns, such as but not limited to mines and underground formations.The air may be compressed using electrically powered turbo-compressors.The compressed air stored in these pockets may be later fed to, e.g.,gas-fired turbine generators to generate electricity during on-peak,higher-priced time periods. In another example, the compressed air isexpanded using turbo expanders or air engines that are drivingelectrical generators to generate electricity.

In another example, the thermal storage capacity of compressed air canbe used according to the principles herein as an energy storage asset.

Using a heat exchanger, it is possible to extract waste heat from thelubricant coolers used in types of compressors, and use the waste heatto produce hot water. Depending on its design, a heat exchanger canproduce non-potable or potable water. When hot water is not required,the lubricant can be routed to the standard components for lubricantcooling. The hot water can be used in central heating or boiler systems,or any other application where hot water is required. Heat exchangersalso offer an opportunity to produce hot air and hot water, and allowthe operator some flexibility to vary the hot air to hot water ratio.

Controller For An Energy Storage Asset

The controllers for the energy storage assets described herein can beused to vary the input to or output from the energy storage assets. Whenthe controller functions as a converter, it converts the AC signal to aDC signal. That DC signal may be used to charge the energy storageasset. When the controller functions as an inverter, it converts onetype of voltage (direct current (DC)) into another type of voltage(alternating current (AC)). Since the electricity supplier generallysupplies 110 or 220 volts AC on the grid, the conversion may typicallybe from 12 volts DC to 110 or 220 volts AC. In another example, theoutput of the controller may be different, depending on the type of loadon the system. Inverters called utility intertie or grid tie may connectto energy generating assets such as solar panels or wind generator, andcan feed their output directly into the inverter. The inverter outputcan be tied to the grid power.

In a non-limiting example, the inverter takes the DC output from theenergy storage asset and runs it into a number of power switchingtransistors. These transistors are switched on and off to feed oppositesides of a transformer, causing the transformer to think it is gettingan AC signal. Depending on the quality and complexity of the inverter,it may put out a square wave, a “quasi-sine” (sometimes called modifiedsine) wave, or a true sine wave. The quality of the quasi-sine wave canvary among different inverters, and also may vary somewhat with theload.

The virtual partitioning of the energy storage asset describedfacilitates partitioning between energy and regulation participation.The partitioning can be based on the available capacity of thecontroller (i.e., the inverter/converter). The SOC of the energy storageasset may be used to provide a constraint within the optimization fordetermining the optimal charge/discharge strategy for participation inthese two different markets. As a non-limiting example, an operatingschedule generated according to the principles herein can indicate theoptimal charge/discharge strategy for the controller, including on anhourly basis, in response to or anticipation of projected LMPs. Thebalance of the inverter capacity of the controller may be made availableto the regulation market at its shorter timescales (e.g., at the2-second or minute-by-minute time intervals described above). Theproportion of the controller output (and hence the energy storage asset)committed to the energy market and the remaining proportion of theenergy storage asset committed to the regulation market are co-optimizedbased on the economic benefit derived from the two markets, and subjectto the SOC constraints. The operating schedules generated based on anyof the principles described herein, and in any of the example, cansuggest the proportion of the controller output committed to the energymarket and to the regulation market in a given time interval t (lessthan time period T), and for what length of time. the proportion of thecontroller output committed to the energy market and to the regulationmarket in a given time interval t (less than time period T). Forexample, for a controller with a 1 MWatt inverter capacity, theprinciples herein can be used to generate an operating schedule thatsuggests the proportion of the controller's 1 MWatt inverter capacitythat can be committed to the energy market and to the regulation marketin a given time interval t to generate the energy-related revenue.

Energy Generating Assets

Examples of energy generating asset applicable to the apparatus andmethods herein include photovoltaic cells, fuel cells, gas turbines,diesel generators, flywheels, electric vehicles and wind turbines.

Electric storage has the potential to address some of the attributes ofrenewable energy generation. The intermittent nature of energygenerating assets, including solar generation, may present somedifficulty for grid operators. For example, weather events can makeenergy output of energy generating assets, including photovoltaic cellsor wind turbines, difficult to predict. As renewable generators make upa growing share of regional generation portfolios, grid operators mayrequire greater real-time visibility of distributed generation andbenefit from a resource's ability to control bi-directional power flow.Adding storage to distributed generation achieves new levels ofresponsiveness not seen with existing systems.

According to principles described herein, the operating schedulegenerated for a system that includes a controller, an energy storageasset and an energy generating asset can firm up intermittent renewablegeneration into dispatchable generation. The operating schedule canprovide for renewable generation forecasting based on the forecastedweather conditions.

Dynamic virtualization can be beneficial to sites that utilize bothenergy storage assets and energy generating assets. For example, byintegrating weather data, price forecasts, and expected site load,examples can accurately predict a solar array's output, determine howmuch solar generation should be captured by an energy storage asset, anddispatch the energy storage asset at the time of day that optimizesrevenues derived from wholesale market participation.

By passing energy through an energy storage asset and exhibitingreal-time control, power can be delivered strategically and act as aprice-responsive resource in the various wholesale markets. In effect,storage allows the maturation of energy generating assets as a resourcethat provides discrete power-flow to the grid that is controllable,quantifiable, and dispatchable. Solar power and its generation can becostly. Through dynamic virtualization the value of renewable generationcan be increased by improving the resource with electric storage.

CONCLUSION

While various inventive embodiments have been described and illustratedherein, those of ordinary skill in the art will readily envision avariety of other means and/or structures for performing the functionand/or obtaining the results and/or one or more of the advantagesdescribed herein, and each of such variations and/or modifications isdeemed to be within the scope of the inventive embodiments describedherein. More generally, those skilled in the art will readily appreciatethat all parameters, dimensions, materials, and configurations describedherein are meant to be exemplary and that the actual parameters,dimensions, materials, and/or configurations will depend upon thespecific application or applications for which the inventive teachingsis/are used. Those skilled in the art will recognize, or be able toascertain using no more than routine experimentation, many equivalentsto the specific inventive embodiments described herein. It is,therefore, to be understood that the foregoing embodiments are presentedby way of example only and that inventive embodiments may be practicedotherwise than as specifically described. Inventive embodimentsdisclosed herein are directed to each individual feature, system,article, material, kit, and/or method described herein. In addition, anycombination of two or more such features, systems, articles, materials,kits, and/or methods, if such features, systems, articles, materials,kits, and/or methods are not mutually inconsistent, is included withinthe inventive scope of the present disclosure.

The above-described embodiments of the disclosure can be implemented inany of numerous ways. For example, some embodiments may be implementedvia one or more controllers, which may employ hardware, software or acombination thereof. In some embodiments discussed herein, one or morecontrollers may be implemented, at least in part, as a state machine.

When any aspect of an embodiment is implemented at least in part insoftware, the software code can be executed on any suitable processor orcollection of processors, whether provided in a single device orcomputer or distributed among multiple devices/computers.

In this respect, various aspects of the disclosure, may be embodied atleast in part as a computer readable storage medium (or multiplecomputer readable storage media) (e.g., a computer memory, one or morefloppy discs, compact discs, optical discs, magnetic tapes, flashmemories, circuit configurations in Field Programmable Gate Arrays orother semiconductor devices, or other tangible computer storage mediumor non-transitory medium) encoded with one or more programs that, whenexecuted on one or more computers or other processors, perform methodsthat implement the various embodiments of the technology discussedabove. The computer readable medium or media can be transportable, suchthat the program or programs stored thereon can be loaded onto one ormore different computers or other processors to implement variousaspects of the present technology as discussed above.

The terms “program” or “software” are used herein in a generic sense torefer to any type of computer code or set of computer-executableinstructions that can be employed to program a computer or otherprocessor to implement various aspects of the present technology asdiscussed above. Additionally, it should be appreciated that accordingto one aspect of this embodiment, one or more computer programs thatwhen executed perform methods of the present technology need not resideon a single computer or processor, but may be distributed in a modularfashion amongst a number of different computers or processors toimplement various aspects of the present technology.

Computer-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. Typically the functionality of the program modulesmay be combined or distributed as desired in various embodiments.

Also, the technology described herein may be embodied as a method, ofwhich at least one example has been provided. The acts performed as partof the method may be ordered in any suitable way. Accordingly,embodiments may be constructed in which acts are performed in an orderdifferent than illustrated, which may include performing some actssimultaneously, even though shown as sequential acts in illustrativeembodiments.

All definitions, as defined and used herein, should be understood tocontrol over dictionary definitions, definitions in documentsincorporated by reference, and/or ordinary meanings of the definedterms.

The indefinite articles “a” and “an,” as used herein in thespecification, unless clearly indicated to the contrary, should beunderstood to mean “at least one.”

The phrase “and/or,” as used herein in the specification, should beunderstood to mean “either or both” of the elements so conjoined, i.e.,elements that are conjunctively present in some cases and disjunctivelypresent in other cases. Multiple elements listed with “and/or” should beconstrued in the same fashion, i.e., “one or more” of the elements soconjoined. Other elements may optionally be present other than theelements specifically identified by the “and/or” clause, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, a reference to “A and/or B”, when used inconjunction with open-ended language such as “comprising” can refer, inone embodiment, to A only (optionally including elements other than B);in another embodiment, to B only (optionally including elements otherthan A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

As used herein in the specification, “or” should be understood to havethe same meaning as “and/or” as defined above. For example, whenseparating items in a list, “or” or “and/or” shall be interpreted asbeing inclusive, i.e., the inclusion of at least one, but also includingmore than one, of a number or list of elements, and, optionally,additional unlisted items. Only terms clearly indicated to the contrary,such as “only one of” or “exactly one of,” or, when used in claims,“consisting of,” will refer to the inclusion of exactly one element of anumber or list of elements. In general, the term “or” as used hereinshall only be interpreted as indicating exclusive alternatives (i.e.“one or the other but not both”) when preceded by terms of exclusivity,such as “either,” “one of,” “only one of,” or “exactly one of.”“Consisting essentially of,” when used in claims, shall have itsordinary meaning as used in the field of patent law.

As used herein in the specification and claims, the phrase “at leastone,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

In the specification and claims, all transitional phrases such as“comprising,” “including,” “carrying,” “having,” “containing,”“involving,” “holding,” “composed of,” and the like are to be understoodto be open-ended, i.e., to mean including but not limited to. Only thetransitional phrases “consisting of” and “consisting essentially of”shall be closed or semi-closed transitional phrases, respectively, asset forth in the United States Patent Office Manual of Patent ExaminingProcedures, Section 2111.03.

1. An apparatus for determining a suggested operating schedule for atleast one energy asset operated by an energy customer, the apparatuscomprising: at least one communication interface; at least one memory tostore processing unit-executable instructions and an objective functionfor the at least one energy asset, wherein the at least one energy assetcomprises at least one controllable energy asset, wherein the objectivefunction facilitates a determination of the suggested operating schedulefor the at least one energy asset based at least in part on a physicalmodel of the thermodynamic property of the at least one energy asset andat least in part on data representative of model parameters, and whereinthe model parameters are: (a) an operation characteristic of the atleast one controllable energy asset, and (b) a projected environmentalcondition during time period T; and at least one processing unitcommunicatively coupled to the at least one memory, wherein, uponexecution of the processing unit-executable instructions, the at leastone processing unit: A) prior to time period T, determines the suggestedoperating schedule based on an optimization of the objective functionover time period T, wherein the objective function is determined basedon a dynamic simulation model of the energy profile of the at least oneenergy asset, a customer baseline (CBL) energy profile for the at leastone energy asset, and a forecast wholesale electricity price, over timeperiod T, associated with a wholesale electricity market, wherein thedynamic simulation model is adaptive to physical changes in the at leastone energy asset based at least in part on the physical model of thethermodynamic property of the at least one energy asset, and wherein thedynamic simulation model is trained using the data; and B) controls theat least one communication interface to transmit to the energy customerthe suggested operating schedule determined in A), and/or controls theat least one memory so as to store the determined suggested operatingschedule, wherein the operation of the at least one energy assetaccording to the suggested operating schedule, over a time period T,facilitates generation of energy-related revenue based at least in parton the wholesale electricity market.
 2. The apparatus of claim 1,wherein the CBL energy profile is computed based on applying the dynamicsimulation model to the data representative of the operationcharacteristic of the at least one controllable energy asset, thethermodynamic property of the building asset, and an environmentalcondition, all during a time period T_(A) prior to time period T.
 3. Theapparatus of claim 1, wherein the data representative of the projectedenvironmental condition is data representative of at least one of anambient temperature of the environment in which the building asset islocated; a humidity of the environment in which the building asset islocated; an amount of solar irradiance of the environment in which thebuilding asset is located, an amount of cloud cover of the environmentin which the building asset is located, an outside air temperature, anoutside air humidity, an outside air enthalpy, an outside air wet bulbtemperature, a dewpoint temperature, and a heat index.
 4. The apparatusof claim 1, wherein the at least one energy asset comprises at least onebuilding asset.
 5. The apparatus of claim 4, wherein the physical modelof the thermodynamic property of the at least one building asset isdetermined based on at least in part on state-space model computationsof the at least one building asset, and on data representative of atleast one of an occupancy schedule of the building asset, a relativehumidity of the building asset, a temperature of the building asset, anda lighting level of the building asset.
 6. The apparatus of claim 1,wherein the operation characteristic of the at least one controllableenergy asset is a load use schedule.
 7. The apparatus of claim 6,wherein the load use schedule imposes a maximum allowable load drawn bythe at least one controllable energy asset over a time interval that isless than time period T.
 8. The apparatus of claim 7, wherein the loaduse schedule impose a different value of maximum allowable load atdifferent time intervals during time period T.
 9. The apparatus of claim1, wherein the operation characteristic of the at least one controllableenergy asset is an energy consumption profile as a function of time ofthe at least one controllable energy asset.
 10. The apparatus of claim1, wherein the operation characteristic of the at least one controllableenergy asset is a set point.
 11. The apparatus of claim 1, wherein thephysical model of the thermodynamic property of the at least one energyasset models a zone temperature, and wherein the zone temperature iscomputed based on at least one heat-balance state-space model of the atleast one energy asset.
 12. The apparatus of claim 1, wherein, uponexecution of the processor-executable instructions, the at least oneprocessing unit determines the suggested operating schedule for the atleast one energy asset using the objective function in A) by minimizinga net energy-related cost over the time period T, wherein the net-energyrelated cost is based at least in part on: an electricity consumption bythe at least one controllable energy asset; and the CBL energy profile;and wherein the energy-related revenue available to the energy customeris based at least in part on the minimized net energy-related cost. 13.The apparatus of claim 12, wherein the net energy-related cost isspecified as a difference between an electricity supply cost and ademand response revenue over the time period T.
 14. The apparatus ofclaim 1, wherein the at least one processing unit determines thesuggested operating schedule for the at least one energy asset as atleast one bias signal, as an interruptible load function, or as at leastone use modulation signal.
 15. The apparatus of claim 14, wherein the atleast one processing unit determines the suggested operating schedulefor the at least one energy asset in (A) as at least one bias signal,and controls the at least one communication interface in (B) to transmitto the energy customer the at least one bias signal at different timesduring time period T.
 16. The apparatus of claim 14, wherein the atleast one processing unit controls the at least one communicationinterface to transmit to the energy customer the suggested operatingschedule as at least one use modulation signal, and wherein theoperation of the at least one energy asset according to the at least oneuse modulation signal causes a modulation with time of the load use ofthe controllable energy asset.
 17. The apparatus of claim 1, wherein thedynamic simulation model of the energy profile of the at least oneenergy asset is a semi-linear regression over at least one of the modelparameters.
 18. The apparatus of claim 17, wherein the dynamicsimulation model of the energy profile of the at least one energy assetis a semi-linear regression over at least one of a zone temperature ofthe at least one energy asset, a load schedule of the at least oneenergy asset, the projected environmental condition, and a controlsetpoint of the at least one controllable energy asset.
 19. Theapparatus of claim 18, wherein the zone temperature of the at least oneenergy asset is a semi-linear regression over at least one of theprojected environmental condition, the load schedule of the at least oneenergy asset, and the control setpoint of the at least one controllableenergy asset.
 20. The apparatus of claim 1, wherein: the at least oneenergy asset is at least one building asset; and the at least onecontrollable energy asset comprises at least one heating, ventilationand air conditioning (HV AC) system to control a variable internaltemperature of the at least one building asset. 21-36. (canceled)